Data Science Manager
MTech Systems
•
Atlanta, GA
0
0
0
0
Mid-Senior level
Posted March 27, 2026
Job link
Thinking about this job
Responsibilities
Responsibilities
- You will work closely with Sales, Product Owners, Engineering, and Customers to translate business needs into well-defined technical solutions, and guide models from experimentation through deployment in a cloud-native environment.
- Lead, mentor, and grow a team of data scientists, setting technical direction and best practices
- Partner with Sales, Product Owners, and Customers to translate business requirements into actionable analytical and modeling tasks
- Communicate complex analytical concepts clearly to technical and non-technical stakeholders
- Drive prioritization and roadmap planning for data science initiatives Data Engineering & Pipelines
- Design and oversee scalable data pipelines using PySpark and Databricks
- Ensure data quality, reliability, and performance across batch and streaming workloads
- Collaborate with data engineering and platform teams to operationalize models Modeling & Analytics
- Build and review models for sequence and time-based data , including forecasting, anomaly detection, and temporal pattern recognition
- Apply and guide best practices in feature engineering, model validation, and performance monitoring
- Lead experimentation and iteration to improve model accuracy and business impact
- Ability to perform advanced statistical analysis and modeling such as liner and non-liner regression, sampling, and Markov chains Optimization & Applied Algorithms
- Apply operations research and optimization techniques to real-world problems, including:
- Last-mile delivery and routing
- Knapsack and resource allocation problems
- Graph-based problems (graph coloring, max flow, network optimization)
- Translate optimization outputs into actionable recommendations for business teams Machine Learning Models & Techniques
- Design, develop, and evaluate a wide range of machine learning models , including:
- Classical models (linear/logistic regression, tree based models, gradient boosting)
- Deep learning models for sequence and temporal data (e.g., temporal convolutional networks, RNNs, and transformer-based approaches)
- Probabilistic and statistical models for forecasting and uncertainty estimation
- Apply techniques such as feature engineering, hyperparameter tuning, model selection, and cross validation at scale
- Implement anomaly detection, causal analysis, and signal extraction for operational and telemetry data
- Balance model accuracy, interpretability, performance, and cost in production environments
- Integrate machine learning outputs with optimization and decision support systems Cloud & Production Deployment
- Work within Azure to deploy and maintain data science solutions
- Leverage Azure Functions and Azure Container Apps for scalable, production-grade model serving and workflows
- Ensure models are observable, maintainable, and cost-efficient in production Required Qualifications
- Proven ability to work cross-functionally with Sales, Product, and external Customers
Not Met Priorities
What still needs stronger evidence
Requirements
- Design and oversee scalable data pipelines using PySpark and Databricks
- Ability to perform advanced statistical analysis and modeling such as liner and non-liner regression, sampling, and Markov chains Optimization & Applied Algorithms
- Last-mile delivery and routing
- Knapsack and resource allocation problems
- Graph-based problems (graph coloring, max flow, network optimization)
- Translate optimization outputs into actionable recommendations for business teams Machine Learning Models & Techniques
- Design, develop, and evaluate a wide range of machine learning models , including:
- Classical models (linear/logistic regression, tree based models, gradient boosting)
- Deep learning models for sequence and temporal data (e.g., temporal convolutional networks, RNNs, and transformer-based approaches)
- Probabilistic and statistical models for forecasting and uncertainty estimation
- Apply techniques such as feature engineering, hyperparameter tuning, model selection, and cross validation at scale
- Implement anomaly detection, causal analysis, and signal extraction for operational and telemetry data
- Balance model accuracy, interpretability, performance, and cost in production environments
- Integrate machine learning outputs with optimization and decision support systems Cloud & Production Deployment
- Work within Azure to deploy and maintain data science solutions
- Leverage Azure Functions and Azure Container Apps for scalable, production-grade model serving and workflows
- Ensure models are observable, maintainable, and cost-efficient in production Required Qualifications
- 5+ years of experience in data science, applied machine learning, or advanced analytics
- 2+ years of experience leading or mentoring data science teams
- 5+ years of hands-on‑ experience with a broad set of machine learning models and techniques , including deep learning for timeseries data and ‑production grade‑ model evaluation
- Strong hands-on experience with PySpark and Databricks for large-scale data processing
- Proven ability to work cross-functionally with Sales, Product, and external Customers
- Solid experience with sequence modeling and time-based data
- Practical knowledge of optimization and operations research techniques , including routing, knapsack, graph algorithms, and flow problems
- Experience deploying data science solutions on Azure , including Functions and Container Apps
- Proficiency in Python and SQL Preferred Qualifications
- Experience with MLOps practices (CI/CD for models, monitoring, retraining pipelines)
- Familiarity with streaming platforms (e.g., Kafka, Event Hubs)
- Experience working in logistics, supply chain, pricing, or operational decision systems
- Data pipelines are reliable, scalable, and trusted by downstream consumers
- Models move efficiently from concept to production and deliver measurable business value
- Stakeholders clearly understand and act on data science outputs
- The data science team grows in technical strength, ownership, and impact
Preferred Skills
- Knapsack and resource allocation problems
- Probabilistic and statistical models for forecasting and uncertainty estimation
- Balance model accuracy, interpretability, performance, and cost in production environments
- Leverage Azure Functions and Azure Container Apps for scalable, production-grade model serving and workflows
- Ensure models are observable, maintainable, and cost-efficient in production Required Qualifications
- Strong hands-on experience with PySpark and Databricks for large-scale data processing
- Proficiency in Python and SQL Preferred Qualifications
- Experience with MLOps practices (CI/CD for models, monitoring, retraining pipelines)
- Familiarity with streaming platforms (e.g., Kafka, Event Hubs)
- Experience working in logistics, supply chain, pricing, or operational decision systems
- Data pipelines are reliable, scalable, and trusted by downstream consumers
- Models move efficiently from concept to production and deliver measurable business value
- Stakeholders clearly understand and act on data science outputs
- The data science team grows in technical strength, ownership, and impact
Education
- (Not required) – Advanced degree (MS or PhD) in Data Science, Computer Science, Operations Research, Applied Mathematics, or a related field What Success Looks Like
We are seeking a Data Science Manager to lead a team of data scientists building production-grade models and data products that drive real business outcomes. This role blends technical leadership, hands-on data science, and cross-functional collaboration , with a strong emphasis on scalable data pipelines, advanced modeling for time-based data, and applied optimization problems. You will work closely with Sales, Product Owners, Engineering, and Customers to translate business needs into well-defined technical solutions, and guide models from experimentation through deployment in a cloud-native environment. Key Responsibilities Leadership & Collaboration
Lead, mentor, and grow a team of data scientists, setting technical direction and best practices
Partner with Sales, Product Owners, and Customers to translate business requirements into actionable analytical and modeling tasks
Communicate complex analytical concepts clearly to technical and non-technical stakeholders
Drive prioritization and roadmap planning for data science initiatives Data Engineering & Pipelines
Design and oversee scalable data pipelines using PySpark and Databricks
Ensure data quality, reliability, and performance across batch and streaming workloads
Collaborate with data engineering and platform teams to operationalize models Modeling & Analytics
Build and review models for sequence and time-based data , including forecasting, anomaly detection, and temporal pattern recognition
Apply and guide best practices in feature engineering, model validation, and performance monitoring
Lead experimentation and iteration to improve model accuracy and business impact
Ability to perform advanced statistical analysis and modeling such as liner and non-liner regression, sampling, and Markov chains Optimization & Applied Algorithms
Apply operations research and optimization techniques to real-world problems, including:
Last-mile delivery and routing
Knapsack and resource allocation problems
Graph-based problems (graph coloring, max flow, network optimization)
Translate optimization outputs into actionable recommendations for business teams Machine Learning Models & Techniques
Design, develop, and evaluate a wide range of machine learning models , including:
Classical models (linear/logistic regression, tree based models, gradient boosting)
Deep learning models for sequence and temporal data (e.g., temporal convolutional networks, RNNs, and transformer-based approaches)
Probabilistic and statistical models for forecasting and uncertainty estimation
Apply techniques such as feature engineering, hyperparameter tuning, model selection, and cross validation at scale
Implement anomaly detection, causal analysis, and signal extraction for operational and telemetry data
Balance model accuracy, interpretability, performance, and cost in production environments
Integrate machine learning outputs with optimization and decision support systems Cloud & Production Deployment
Work within Azure to deploy and maintain data science solutions
Leverage Azure Functions and Azure Container Apps for scalable, production-grade model serving and workflows
Ensure models are observable, maintainable, and cost-efficient in production Required Qualifications
5+ years of experience in data science, applied machine learning, or advanced analytics
2+ years of experience leading or mentoring data science teams
5+ years of hands-on‑ experience with a broad set of machine learning models and techniques , including deep learning for timeseries data and ‑production grade‑ model evaluation
Strong hands-on experience with PySpark and Databricks for large-scale data processing
Proven ability to work cross-functionally with Sales, Product, and external Customers
Solid experience with sequence modeling and time-based data
Practical knowledge of optimization and operations research techniques , including routing, knapsack, graph algorithms, and flow problems
Experience deploying data science solutions on Azure , including Functions and Container Apps
Proficiency in Python and SQL Preferred Qualifications
Experience with MLOps practices (CI/CD for models, monitoring, retraining pipelines)
Familiarity with streaming platforms (e.g., Kafka, Event Hubs)
Experience working in logistics, supply chain, pricing, or operational decision systems
Advanced degree (MS or PhD) in Data Science, Computer Science, Operations Research, Applied Mathematics, or a related field What Success Looks Like
Data pipelines are reliable, scalable, and trusted by downstream consumers
Models move efficiently from concept to production and deliver measurable business value
Stakeholders clearly understand and act on data science outputs
The data science team grows in technical strength, ownership, and impact
Lead, mentor, and grow a team of data scientists, setting technical direction and best practices
Partner with Sales, Product Owners, and Customers to translate business requirements into actionable analytical and modeling tasks
Communicate complex analytical concepts clearly to technical and non-technical stakeholders
Drive prioritization and roadmap planning for data science initiatives Data Engineering & Pipelines
Design and oversee scalable data pipelines using PySpark and Databricks
Ensure data quality, reliability, and performance across batch and streaming workloads
Collaborate with data engineering and platform teams to operationalize models Modeling & Analytics
Build and review models for sequence and time-based data , including forecasting, anomaly detection, and temporal pattern recognition
Apply and guide best practices in feature engineering, model validation, and performance monitoring
Lead experimentation and iteration to improve model accuracy and business impact
Ability to perform advanced statistical analysis and modeling such as liner and non-liner regression, sampling, and Markov chains Optimization & Applied Algorithms
Apply operations research and optimization techniques to real-world problems, including:
Last-mile delivery and routing
Knapsack and resource allocation problems
Graph-based problems (graph coloring, max flow, network optimization)
Translate optimization outputs into actionable recommendations for business teams Machine Learning Models & Techniques
Design, develop, and evaluate a wide range of machine learning models , including:
Classical models (linear/logistic regression, tree based models, gradient boosting)
Deep learning models for sequence and temporal data (e.g., temporal convolutional networks, RNNs, and transformer-based approaches)
Probabilistic and statistical models for forecasting and uncertainty estimation
Apply techniques such as feature engineering, hyperparameter tuning, model selection, and cross validation at scale
Implement anomaly detection, causal analysis, and signal extraction for operational and telemetry data
Balance model accuracy, interpretability, performance, and cost in production environments
Integrate machine learning outputs with optimization and decision support systems Cloud & Production Deployment
Work within Azure to deploy and maintain data science solutions
Leverage Azure Functions and Azure Container Apps for scalable, production-grade model serving and workflows
Ensure models are observable, maintainable, and cost-efficient in production Required Qualifications
5+ years of experience in data science, applied machine learning, or advanced analytics
2+ years of experience leading or mentoring data science teams
5+ years of hands-on‑ experience with a broad set of machine learning models and techniques , including deep learning for timeseries data and ‑production grade‑ model evaluation
Strong hands-on experience with PySpark and Databricks for large-scale data processing
Proven ability to work cross-functionally with Sales, Product, and external Customers
Solid experience with sequence modeling and time-based data
Practical knowledge of optimization and operations research techniques , including routing, knapsack, graph algorithms, and flow problems
Experience deploying data science solutions on Azure , including Functions and Container Apps
Proficiency in Python and SQL Preferred Qualifications
Experience with MLOps practices (CI/CD for models, monitoring, retraining pipelines)
Familiarity with streaming platforms (e.g., Kafka, Event Hubs)
Experience working in logistics, supply chain, pricing, or operational decision systems
Advanced degree (MS or PhD) in Data Science, Computer Science, Operations Research, Applied Mathematics, or a related field What Success Looks Like
Data pipelines are reliable, scalable, and trusted by downstream consumers
Models move efficiently from concept to production and deliver measurable business value
Stakeholders clearly understand and act on data science outputs
The data science team grows in technical strength, ownership, and impact