Associate Principal/Forensic AI Engineer (Forensic Services practice)
Charles River Associates
•
Boston, MA
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0
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Not Applicable
Posted April 5, 2026
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Responsibilities
Commitments
Responsibilities
- Support our dev team in building unique AI-based solutions for our clients.
- Lead and support technical vision and execution for forensic investigations involving AI-generated content, large language model misuse, deepfake media, synthetic voice and video, and AI-enabled fraud or misconduct.
- Develop, deploy, and validate deepfake detection pipelines and multimodal AI forensic tooling to analyze and authenticate text, image, audio, and video evidence at scale.
- Perform AI content attribution and provenance analysis, including authorship attribution for LLM-generated text, model fingerprinting, training data inference, and C2PA (Coalition for Content Provenance and Authenticity) manifest analysis.
- Apply LLM-based analytical frameworks, including retrieval-augmented generation (RAG) pipelines, structured output generation, and document intelligence, to accelerate investigation workflows and enhance analytical throughput.
- Conduct adversarial prompt analysis, prompt injection detection, and evaluation of AI system vulnerabilities in the context of client incidents, regulatory matters, and litigation.
- Design and build forensic data pipelines and investigatory tooling used to process and analyze large and varied datasets, including unstructured text corpora, media archives, model outputs, and system logs.
- Serve as technical subject matter expert advising legal counsel and corporate executives on complex AI, generative media, and data integrity challenges , translating sophisticated technical findings into defensible, plain-language expert reports and testimony suitable for judicial and regulatory audiences.
- Deliver training programs for clients and internal colleagues on responsible LLM use, AI-generated content identification methodologies, forensic readiness, and AI governance frameworks.
- Lead cross-functional engagements requiring coordination across technical analysis, legal strategy, digital forensics, and stakeholder communication under aggressive deadlines.
- Mentor junior team members.
- Contribute to internal initiatives, thought leadership, and practice development.
Commitments
To be considered for a position in the United States , we require the following:
Resume – please include current address, personal email and telephone number;
If you are interested in applying for one of our international locations , please visit our Careers site to view and apply for available jobs.
CRA’s robust skills development programs, including a commitment to offering 100 hours of training annually through formal and informal programs, encourage you to thrive as an individual and team member.
Beginning with research and analysis skill building, training continues with technical training, presentation skills, internal seminars, and career mentoring and performance coaching from an assigned senior colleague.
Additional leadership and collaboration opportunities exist through internal firm development activities.
Work Location Flexibility
CRA creates a work environment that enables our colleagues to benefit from being together in the office to best deliver on our promise of career growth, mentorship and inclusivity.
At the same time, we recognize that individuals realize a range of benefits when working from home periodically.
We currently expect that individuals spend at least 3 to 4 days a week working in the office (which may include traveling to another CRA office or to client meetings), with specific days determined in coordination with your practice or team.
Our Commitment to Equal Employment Opportunity
Not Met Priorities
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Requirements
- 8–10+ years of progressive experience in machine learning engineering, AI research, digital forensics, data science, or a closely related technical field, with deep expertise in at least two of the following domains:
- Development, fine-tuning, evaluation, or production deployment of large language models (LLMs) or generative AI systems
- Synthetic media detection, deepfake analysis, or AI-generated content forensics
- Natural language processing, computational linguistics, or authorship attribution
- Digital forensics, incident response, eDiscovery, or cybercrime investigation
- Consulting delivery, expert witness services, or client-facing technical advisory roles in a litigation or regulatory context
- A representative portfolio of project contributions , including open-source contributions, published research, technical blog posts, or other observable works , demonstrating sustained, applied AI proficiency across investigation-relevant domains.
- Demonstrated ability to conduct or support expert witness engagements, produce legally defensible forensic reports, and communicate complex technical findings to non-technical audiences including judges, regulators, and corporate executives.
- Familiarity with AI governance and risk management frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001:2023 (AI Management Systems), and the OWASP Generative AI Security guidelines.
- Technical Skills
- LLM Proficiency: Deep understanding of large language model architectures (transformer-based models including GPT, LLaMA, Mistral, Gemma, and related families); proficiency with fine-tuning methodologies including LoRA, QLoRA, and instruction tuning; and experience with alignment techniques (RLHF, RLAIF, DPO).
- LLM Ecosystems and Tooling: Proficiency with the HuggingFace ecosystem (Transformers, PEFT, Datasets, Evaluate, TRL); major LLM inference frameworks (vLLM, llama.cpp, Ollama); and orchestration frameworks including LangChain and LlamaIndex.
- Retrieval-Augmented Generation (RAG) Pipelines: Experience building, evaluating, and optimizing RAG pipelines using vector databases (Pinecone, Weaviate, ChromaDB, pgvector) and embedding models, including chunking strategy, retrieval evaluation, and hybrid search.
- Deepfake Detection and Synthetic Media Forensics: Competency in deepfake detection methodologies including CNN- and transformer-based detection models (trained on FaceForensics++, DFDC, or equivalent datasets); GAN architecture analysis; multimodal artifact inspection (lip synchronization, temporal consistency, audio-visual misalignment); and pixel-level manipulation detection tools (e.g., Amped Authenticate, Sensity AI).
- Content Provenance and Authentication: Understanding of content provenance standards including C2PA (Coalition for Content Provenance and Authenticity), cryptographic content credentials, digital watermarking approaches (including SynthID), and blockchain-based authenticity verification.
- Text Forensics and Authorship Attribution: Experience with NLP-based forensic methodologies including stylometric analysis, semantic embedding-based authorship attribution, human vs. machine-generated text classification, and LLM source attribution (model fingerprinting, training data membership inference).
- Programming Languages and Core Libraries: Advanced proficiency in Python; competency with PyTorch and/or TensorFlow for deep learning model development and evaluation; data analysis using pandas, NumPy, and scikit-learn; and SQL for structured data querying and investigation support.
- Computer Vision and Multimodal Analysis: Experience with computer vision libraries (OpenCV, torchvision, PIL/Pillow) and audio/video processing tools for media forensics, including EXIF metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
- Cloud and Infrastructure: Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure Machine Learning) and containerization approaches (Docker, Kubernetes) for deploying and managing AI forensic tooling in enterprise environments.
- Data Engineering: Experience with data engineering frameworks and tools including Apache Spark, Airflow, and modern data warehousing platforms (Snowflake, BigQuery, Redshift) for processing and analyzing large-scale unstructured and semi-structured datasets.
Preferred Skills
- LLM Proficiency: Deep understanding of large language model architectures (transformer-based models including GPT, LLaMA, Mistral, Gemma, and related families); proficiency with fine-tuning methodologies including LoRA, QLoRA, and instruction tuning; and experience with alignment techniques (RLHF, RLAIF, DPO).
- LLM Ecosystems and Tooling: Proficiency with the HuggingFace ecosystem (Transformers, PEFT, Datasets, Evaluate, TRL); major LLM inference frameworks (vLLM, llama.cpp, Ollama); and orchestration frameworks including LangChain and LlamaIndex.
- Content Provenance and Authentication: Understanding of content provenance standards including C2PA (Coalition for Content Provenance and Authenticity), cryptographic content credentials, digital watermarking approaches (including SynthID), and blockchain-based authenticity verification.
- Text Forensics and Authorship Attribution: Experience with NLP-based forensic methodologies including stylometric analysis, semantic embedding-based authorship attribution, human vs. machine-generated text classification, and LLM source attribution (model fingerprinting, training data membership inference).
- Programming Languages and Core Libraries: Advanced proficiency in Python; competency with PyTorch and/or TensorFlow for deep learning model development and evaluation; data analysis using pandas, NumPy, and scikit-learn; and SQL for structured data querying and investigation support.
- Computer Vision and Multimodal Analysis: Experience with computer vision libraries (OpenCV, torchvision, PIL/Pillow) and audio/video processing tools for media forensics, including EXIF metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
- Cloud and Infrastructure: Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure Machine Learning) and containerization approaches (Docker, Kubernetes) for deploying and managing AI forensic tooling in enterprise environments.
- Data Engineering: Experience with data engineering frameworks and tools including Apache Spark, Airflow, and modern data warehousing platforms (Snowflake, BigQuery, Redshift) for processing and analyzing large-scale unstructured and semi-structured datasets.
Education
- (Not required) – Education
- (Required) – Bachelor’s degree required; Computer Science, Electrical Engineering, Data Science, Computational Linguistics, Information Systems, or a related technical field.
- (Not required) – Graduate degree (M.S. or Ph.D.) in Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a closely related discipline preferred.
About Charles River Associates
CRA is a leading global consulting firm that provides independent economic and financial analysis behind litigation matters, guides businesses through critical strategy and operational issues to become more profitable, and advises governments on the economic impact of policies and regulations. Our two main services – economic and management consulting – are delivered by practice groups that focus on specific areas of expertise or industries. Click here to learn how CRA can help you launch your career.
Position Overview
CRA’s Forensic Services practice supports companies’ commitment to integrity by assisting them and their counsel in independently responding to allegations of fraud, waste, abuse, misconduct, and non-compliance. We are noted for deploying cross-trained teams of forensic professionals to assist our clients in gaining deeper insights and greater value more quickly. We provide technical development, expert, and forensic services as well as cybercrime investigation services.
As an Associate Principal, you will lead projects that sit at the intersection of artificial intelligence, full stack development, forensic investigation, synthetic media analysis, and litigation support. In this role, you will serve as a key subject matter expert, builder, and technical advisor across a portfolio of unique client problems , including matters involving large language model (LLM) misuse, AI-generated content authentication, deepfake detection, and AI-enabled fraud. You will lead, learn from, and work alongside a team of like-minded, supportive, and highly technical colleagues.
A day in the life consists of collaborating across client matters, supporting forensic investigations, advising upon AI governance and synthetic media standards, building and deploying detection tooling, conducting structured and unstructured data analysis, and staying current with rapidly evolving developments in the generative AI and AI forensics space.
As An Associate Principal, You Will
Support our dev team in building unique AI-based solutions for our clients.
Lead and support technical vision and execution for forensic investigations involving AI-generated content, large language model misuse, deepfake media, synthetic voice and video, and AI-enabled fraud or misconduct.
Develop, deploy, and validate deepfake detection pipelines and multimodal AI forensic tooling to analyze and authenticate text, image, audio, and video evidence at scale.
Perform AI content attribution and provenance analysis, including authorship attribution for LLM-generated text, model fingerprinting, training data inference, and C2PA (Coalition for Content Provenance and Authenticity) manifest analysis.
Apply LLM-based analytical frameworks, including retrieval-augmented generation (RAG) pipelines, structured output generation, and document intelligence, to accelerate investigation workflows and enhance analytical throughput.
Conduct adversarial prompt analysis, prompt injection detection, and evaluation of AI system vulnerabilities in the context of client incidents, regulatory matters, and litigation.
Design and build forensic data pipelines and investigatory tooling used to process and analyze large and varied datasets, including unstructured text corpora, media archives, model outputs, and system logs.
Serve as technical subject matter expert advising legal counsel and corporate executives on complex AI, generative media, and data integrity challenges , translating sophisticated technical findings into defensible, plain-language expert reports and testimony suitable for judicial and regulatory audiences.
Deliver training programs for clients and internal colleagues on responsible LLM use, AI-generated content identification methodologies, forensic readiness, and AI governance frameworks.
Lead cross-functional engagements requiring coordination across technical analysis, legal strategy, digital forensics, and stakeholder communication under aggressive deadlines.
Mentor junior team members.
Contribute to internal initiatives, thought leadership, and practice development.
Education
Bachelor’s degree required; Computer Science, Electrical Engineering, Data Science, Computational Linguistics, Information Systems, or a related technical field.
Graduate degree (M.S. or Ph.D.) in Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a closely related discipline preferred.
Experience
8–10+ years of progressive experience in machine learning engineering, AI research, digital forensics, data science, or a closely related technical field, with deep expertise in at least two of the following domains:
Development, fine-tuning, evaluation, or production deployment of large language models (LLMs) or generative AI systems
Synthetic media detection, deepfake analysis, or AI-generated content forensics
Natural language processing, computational linguistics, or authorship attribution
Digital forensics, incident response, eDiscovery, or cybercrime investigation
Consulting delivery, expert witness services, or client-facing technical advisory roles in a litigation or regulatory context
A representative portfolio of project contributions , including open-source contributions, published research, technical blog posts, or other observable works , demonstrating sustained, applied AI proficiency across investigation-relevant domains.
Demonstrated ability to conduct or support expert witness engagements, produce legally defensible forensic reports, and communicate complex technical findings to non-technical audiences including judges, regulators, and corporate executives.
Familiarity with AI governance and risk management frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001:2023 (AI Management Systems), and the OWASP Generative AI Security guidelines.
Technical Skills
LLM Proficiency: Deep understanding of large language model architectures (transformer-based models including GPT, LLaMA, Mistral, Gemma, and related families); proficiency with fine-tuning methodologies including LoRA, QLoRA, and instruction tuning; and experience with alignment techniques (RLHF, RLAIF, DPO).
LLM Ecosystems and Tooling: Proficiency with the HuggingFace ecosystem (Transformers, PEFT, Datasets, Evaluate, TRL); major LLM inference frameworks (vLLM, llama.cpp, Ollama); and orchestration frameworks including LangChain and LlamaIndex.
Retrieval-Augmented Generation (RAG) Pipelines: Experience building, evaluating, and optimizing RAG pipelines using vector databases (Pinecone, Weaviate, ChromaDB, pgvector) and embedding models, including chunking strategy, retrieval evaluation, and hybrid search.
Deepfake Detection and Synthetic Media Forensics: Competency in deepfake detection methodologies including CNN- and transformer-based detection models (trained on FaceForensics++, DFDC, or equivalent datasets); GAN architecture analysis; multimodal artifact inspection (lip synchronization, temporal consistency, audio-visual misalignment); and pixel-level manipulation detection tools (e.g., Amped Authenticate, Sensity AI).
Content Provenance and Authentication: Understanding of content provenance standards including C2PA (Coalition for Content Provenance and Authenticity), cryptographic content credentials, digital watermarking approaches (including SynthID), and blockchain-based authenticity verification.
Text Forensics and Authorship Attribution: Experience with NLP-based forensic methodologies including stylometric analysis, semantic embedding-based authorship attribution, human vs. machine-generated text classification, and LLM source attribution (model fingerprinting, training data membership inference).
Programming Languages and Core Libraries: Advanced proficiency in Python; competency with PyTorch and/or TensorFlow for deep learning model development and evaluation; data analysis using pandas, NumPy, and scikit-learn; and SQL for structured data querying and investigation support.
Computer Vision and Multimodal Analysis: Experience with computer vision libraries (OpenCV, torchvision, PIL/Pillow) and audio/video processing tools for media forensics, including EXIF metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
Cloud and Infrastructure: Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure Machine Learning) and containerization approaches (Docker, Kubernetes) for deploying and managing AI forensic tooling in enterprise environments.
Data Engineering: Experience with data engineering frameworks and tools including Apache Spark, Airflow, and modern data warehousing platforms (Snowflake, BigQuery, Redshift) for processing and analyzing large-scale unstructured and semi-structured datasets.
To Apply
To be considered for a position in the United States , we require the following:
Resume – please include current address, personal email and telephone number;
If you are interested in applying for one of our international locations , please visit our Careers site to view and apply for available jobs.
Career Growth And Benefits
CRA’s robust skills development programs, including a commitment to offering 100 hours of training annually through formal and informal programs, encourage you to thrive as an individual and team member. Beginning with research and analysis skill building, training continues with technical training, presentation skills, internal seminars, and career mentoring and performance coaching from an assigned senior colleague. Additional leadership and collaboration opportunities exist through internal firm development activities.
We offer a comprehensive total rewards program including a superior benefits package, wellness programming to support physical, mental, emotional and financial well-being, and in-house immigration support for foreign nationals and international business travelers.
Work Location Flexibility
CRA creates a work environment that enables our colleagues to benefit from being together in the office to best deliver on our promise of career growth, mentorship and inclusivity. At the same time, we recognize that individuals realize a range of benefits when working from home periodically. We currently expect that individuals spend at least 3 to 4 days a week working in the office (which may include traveling to another CRA office or to client meetings), with specific days determined in coordination with your practice or team.
Our Commitment to Equal Employment Opportunity
Charles River Associates is an equal opportunity employer (EOE). All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, status as a protected veteran, or any other protected characteristic under applicable law.
Salary And Other Compensation
A good-faith estimate of the annual base salary range for this position is $140,000 - $170,000. Stating pay within this range may vary based on factors such as education level, experience, skills, geographic location, market conditions, and other qualifications of the successful candidate. This position may be eligible for additional bonus incentive compensation.
CRA offers a comprehensive benefits package, subject to eligibility requirements, which may include: medical, dental, and vision insurance; 401(k) retirement plan with employer match; life and disability insurance; paid time off (vacation, sick leave, holidays); paid parental leave; wellness programs and employee assistance resources; and commuter benefits.
CRA is a leading global consulting firm that provides independent economic and financial analysis behind litigation matters, guides businesses through critical strategy and operational issues to become more profitable, and advises governments on the economic impact of policies and regulations. Our two main services – economic and management consulting – are delivered by practice groups that focus on specific areas of expertise or industries. Click here to learn how CRA can help you launch your career.
Position Overview
CRA’s Forensic Services practice supports companies’ commitment to integrity by assisting them and their counsel in independently responding to allegations of fraud, waste, abuse, misconduct, and non-compliance. We are noted for deploying cross-trained teams of forensic professionals to assist our clients in gaining deeper insights and greater value more quickly. We provide technical development, expert, and forensic services as well as cybercrime investigation services.
As an Associate Principal, you will lead projects that sit at the intersection of artificial intelligence, full stack development, forensic investigation, synthetic media analysis, and litigation support. In this role, you will serve as a key subject matter expert, builder, and technical advisor across a portfolio of unique client problems , including matters involving large language model (LLM) misuse, AI-generated content authentication, deepfake detection, and AI-enabled fraud. You will lead, learn from, and work alongside a team of like-minded, supportive, and highly technical colleagues.
A day in the life consists of collaborating across client matters, supporting forensic investigations, advising upon AI governance and synthetic media standards, building and deploying detection tooling, conducting structured and unstructured data analysis, and staying current with rapidly evolving developments in the generative AI and AI forensics space.
As An Associate Principal, You Will
Support our dev team in building unique AI-based solutions for our clients.
Lead and support technical vision and execution for forensic investigations involving AI-generated content, large language model misuse, deepfake media, synthetic voice and video, and AI-enabled fraud or misconduct.
Develop, deploy, and validate deepfake detection pipelines and multimodal AI forensic tooling to analyze and authenticate text, image, audio, and video evidence at scale.
Perform AI content attribution and provenance analysis, including authorship attribution for LLM-generated text, model fingerprinting, training data inference, and C2PA (Coalition for Content Provenance and Authenticity) manifest analysis.
Apply LLM-based analytical frameworks, including retrieval-augmented generation (RAG) pipelines, structured output generation, and document intelligence, to accelerate investigation workflows and enhance analytical throughput.
Conduct adversarial prompt analysis, prompt injection detection, and evaluation of AI system vulnerabilities in the context of client incidents, regulatory matters, and litigation.
Design and build forensic data pipelines and investigatory tooling used to process and analyze large and varied datasets, including unstructured text corpora, media archives, model outputs, and system logs.
Serve as technical subject matter expert advising legal counsel and corporate executives on complex AI, generative media, and data integrity challenges , translating sophisticated technical findings into defensible, plain-language expert reports and testimony suitable for judicial and regulatory audiences.
Deliver training programs for clients and internal colleagues on responsible LLM use, AI-generated content identification methodologies, forensic readiness, and AI governance frameworks.
Lead cross-functional engagements requiring coordination across technical analysis, legal strategy, digital forensics, and stakeholder communication under aggressive deadlines.
Mentor junior team members.
Contribute to internal initiatives, thought leadership, and practice development.
Education
Bachelor’s degree required; Computer Science, Electrical Engineering, Data Science, Computational Linguistics, Information Systems, or a related technical field.
Graduate degree (M.S. or Ph.D.) in Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a closely related discipline preferred.
Experience
8–10+ years of progressive experience in machine learning engineering, AI research, digital forensics, data science, or a closely related technical field, with deep expertise in at least two of the following domains:
Development, fine-tuning, evaluation, or production deployment of large language models (LLMs) or generative AI systems
Synthetic media detection, deepfake analysis, or AI-generated content forensics
Natural language processing, computational linguistics, or authorship attribution
Digital forensics, incident response, eDiscovery, or cybercrime investigation
Consulting delivery, expert witness services, or client-facing technical advisory roles in a litigation or regulatory context
A representative portfolio of project contributions , including open-source contributions, published research, technical blog posts, or other observable works , demonstrating sustained, applied AI proficiency across investigation-relevant domains.
Demonstrated ability to conduct or support expert witness engagements, produce legally defensible forensic reports, and communicate complex technical findings to non-technical audiences including judges, regulators, and corporate executives.
Familiarity with AI governance and risk management frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001:2023 (AI Management Systems), and the OWASP Generative AI Security guidelines.
Technical Skills
LLM Proficiency: Deep understanding of large language model architectures (transformer-based models including GPT, LLaMA, Mistral, Gemma, and related families); proficiency with fine-tuning methodologies including LoRA, QLoRA, and instruction tuning; and experience with alignment techniques (RLHF, RLAIF, DPO).
LLM Ecosystems and Tooling: Proficiency with the HuggingFace ecosystem (Transformers, PEFT, Datasets, Evaluate, TRL); major LLM inference frameworks (vLLM, llama.cpp, Ollama); and orchestration frameworks including LangChain and LlamaIndex.
Retrieval-Augmented Generation (RAG) Pipelines: Experience building, evaluating, and optimizing RAG pipelines using vector databases (Pinecone, Weaviate, ChromaDB, pgvector) and embedding models, including chunking strategy, retrieval evaluation, and hybrid search.
Deepfake Detection and Synthetic Media Forensics: Competency in deepfake detection methodologies including CNN- and transformer-based detection models (trained on FaceForensics++, DFDC, or equivalent datasets); GAN architecture analysis; multimodal artifact inspection (lip synchronization, temporal consistency, audio-visual misalignment); and pixel-level manipulation detection tools (e.g., Amped Authenticate, Sensity AI).
Content Provenance and Authentication: Understanding of content provenance standards including C2PA (Coalition for Content Provenance and Authenticity), cryptographic content credentials, digital watermarking approaches (including SynthID), and blockchain-based authenticity verification.
Text Forensics and Authorship Attribution: Experience with NLP-based forensic methodologies including stylometric analysis, semantic embedding-based authorship attribution, human vs. machine-generated text classification, and LLM source attribution (model fingerprinting, training data membership inference).
Programming Languages and Core Libraries: Advanced proficiency in Python; competency with PyTorch and/or TensorFlow for deep learning model development and evaluation; data analysis using pandas, NumPy, and scikit-learn; and SQL for structured data querying and investigation support.
Computer Vision and Multimodal Analysis: Experience with computer vision libraries (OpenCV, torchvision, PIL/Pillow) and audio/video processing tools for media forensics, including EXIF metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
Cloud and Infrastructure: Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure Machine Learning) and containerization approaches (Docker, Kubernetes) for deploying and managing AI forensic tooling in enterprise environments.
Data Engineering: Experience with data engineering frameworks and tools including Apache Spark, Airflow, and modern data warehousing platforms (Snowflake, BigQuery, Redshift) for processing and analyzing large-scale unstructured and semi-structured datasets.
To Apply
To be considered for a position in the United States , we require the following:
Resume – please include current address, personal email and telephone number;
If you are interested in applying for one of our international locations , please visit our Careers site to view and apply for available jobs.
Career Growth And Benefits
CRA’s robust skills development programs, including a commitment to offering 100 hours of training annually through formal and informal programs, encourage you to thrive as an individual and team member. Beginning with research and analysis skill building, training continues with technical training, presentation skills, internal seminars, and career mentoring and performance coaching from an assigned senior colleague. Additional leadership and collaboration opportunities exist through internal firm development activities.
We offer a comprehensive total rewards program including a superior benefits package, wellness programming to support physical, mental, emotional and financial well-being, and in-house immigration support for foreign nationals and international business travelers.
Work Location Flexibility
CRA creates a work environment that enables our colleagues to benefit from being together in the office to best deliver on our promise of career growth, mentorship and inclusivity. At the same time, we recognize that individuals realize a range of benefits when working from home periodically. We currently expect that individuals spend at least 3 to 4 days a week working in the office (which may include traveling to another CRA office or to client meetings), with specific days determined in coordination with your practice or team.
Our Commitment to Equal Employment Opportunity
Charles River Associates is an equal opportunity employer (EOE). All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, status as a protected veteran, or any other protected characteristic under applicable law.
Salary And Other Compensation
A good-faith estimate of the annual base salary range for this position is $140,000 - $170,000. Stating pay within this range may vary based on factors such as education level, experience, skills, geographic location, market conditions, and other qualifications of the successful candidate. This position may be eligible for additional bonus incentive compensation.
CRA offers a comprehensive benefits package, subject to eligibility requirements, which may include: medical, dental, and vision insurance; 401(k) retirement plan with employer match; life and disability insurance; paid time off (vacation, sick leave, holidays); paid parental leave; wellness programs and employee assistance resources; and commuter benefits.