Senior MAQR (ML & Quantitative Research) Data Scientist
TWG AI
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Santa Monica, CA
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Mid-Senior level
Posted April 2, 2026
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Responsibilities
Commitments
Responsibilities
- Algorithm to Production
- Own the full model lifecycle: from problem formulation and feature engineering through training, validation, and deployment in a live production environment
- Build and maintain ML pipelines that run reliably — not just models that score well offline
- Instrument models for monitoring, drift detection, and retraining triggers in production systems
- Work closely with engineers to integrate models into product workflows, operational tooling, and client-facing applications
- Applied Science & Experimentation
- Develop predictive models, time series forecasts, anomaly detection systems, and causal inference frameworks across varied business contexts
- Design and analyze experiments — from A/B tests to geo-randomized trials — with proper statistical rigor
- Translate ambiguous business problems into well-scoped data science problems; define success metrics before modeling begins
- Review relevant literature and apply state-of-the-art methods where warranted; resist overengineering where simpler approaches perform just as well
- Collaboration & Communication
- Partner with cross-functional teams — business leads, engineers, product managers, and analysts — to deliver solutions aligned with real objectives
- Translate model outputs and technical trade-offs into clear, accessible communication for business stakeholders
- Contribute to AI governance standards and champion responsible, explainable AI practices across client engagements
Commitments
This position is located in Santa Monica, CA.
Not Met Priorities
What still needs stronger evidence
Requirements
- At least 6+ years of Python (or equivalent) for data analysis, model development, and production-ready scripting
- At least 4+ years of end-to-end ML model development and deployment — not just training, but shipping to production
- Demonstrated fluency across the data science stack: wrangling, feature engineering, modeling, evaluation, and deployment tooling
- Strong statistical foundations: hypothesis testing, regression, Bayesian reasoning, model validation, and uncertainty quantification
- Proven ability to bring definition and structure to large, ambiguous problems
- Clear communicator who can explain model trade-offs to an engineer and business impact to an executive
Preferred Skills
- Experience with cloud platforms and cloud-native ML workflows
- MLOps familiarity: pipeline orchestration, model registries, CI/CD for ML, and inference infrastructure
- Experience with large-scale or real-time data (streaming, event-driven, high-volume batch processing)
- Background in time series forecasting, causal inference, or combinatorial optimization
- Experience in financial services, insurance, marketplace integrity, or operations-heavy industries
- Knowledge of quantitative finance, econometrics, or portfolio methods — useful context, not a requirement
- Ability to write production-quality, maintainable code beyond model scripts
- Experience working Agile with cross-functional, client-facing teams
Education
- (Not required) – Master's degree in a quantitative field (Statistics, Computer Science, Mathematics, Operations Research, Engineering, or related) plus 6+ years of applied data science experience, OR PhD in a quantitative field plus 3+ years of hands-on industry experience
At TWG Group Holdings, LLC ("TWG Global"), we drive innovation and business transformation across a range of industries—including financial services, insurance, technology, media, and sports—by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees.
We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development.
You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation.
At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses.
The Role
Join our growing Machine Learning, AI & Quantitative Research (MAQR) group as a Senior Data Scientist. MAQR is a small, high-ownership team embedded across a diverse portfolio of businesses — from financial services and insurance to marketplace trading platforms and operations. We don't have a narrow mandate. We build what needs to be built, ship it to production, and measure what matters.
This role is for a data scientist who is as comfortable wrangling a messy operational dataset as they are designing a production inference pipeline. You bring rigor without rigidity — you know when a logistic regression is the right answer and when it isn't. You've built things that run in the real world, not just in notebooks.
We operate with a startup mindset inside a well-capitalized holding company. You will have real ownership, real clients, and real impact.
Key Responsibilities:
Algorithm to Production
Own the full model lifecycle: from problem formulation and feature engineering through training, validation, and deployment in a live production environment
Build and maintain ML pipelines that run reliably — not just models that score well offline
Instrument models for monitoring, drift detection, and retraining triggers in production systems
Work closely with engineers to integrate models into product workflows, operational tooling, and client-facing applications
Applied Science & Experimentation
Develop predictive models, time series forecasts, anomaly detection systems, and causal inference frameworks across varied business contexts
Design and analyze experiments — from A/B tests to geo-randomized trials — with proper statistical rigor
Translate ambiguous business problems into well-scoped data science problems; define success metrics before modeling begins
Review relevant literature and apply state-of-the-art methods where warranted; resist overengineering where simpler approaches perform just as well
Collaboration & Communication
Partner with cross-functional teams — business leads, engineers, product managers, and analysts — to deliver solutions aligned with real objectives
Translate model outputs and technical trade-offs into clear, accessible communication for business stakeholders
Contribute to AI governance standards and champion responsible, explainable AI practices across client engagements
Requirements
Master's degree in a quantitative field (Statistics, Computer Science, Mathematics, Operations Research, Engineering, or related) plus 6+ years of applied data science experience, OR PhD in a quantitative field plus 3+ years of hands-on industry experience
At least 6+ years of Python (or equivalent) for data analysis, model development, and production-ready scripting
At least 4+ years of end-to-end ML model development and deployment — not just training, but shipping to production
Demonstrated fluency across the data science stack: wrangling, feature engineering, modeling, evaluation, and deployment tooling
Strong statistical foundations: hypothesis testing, regression, Bayesian reasoning, model validation, and uncertainty quantification
Proven ability to bring definition and structure to large, ambiguous problems
Clear communicator who can explain model trade-offs to an engineer and business impact to an executive
Preferred qualifications:
Experience with cloud platforms and cloud-native ML workflows
MLOps familiarity: pipeline orchestration, model registries, CI/CD for ML, and inference infrastructure
Experience with large-scale or real-time data (streaming, event-driven, high-volume batch processing)
Background in time series forecasting, causal inference, or combinatorial optimization
Experience in financial services, insurance, marketplace integrity, or operations-heavy industries
Knowledge of quantitative finance, econometrics, or portfolio methods — useful context, not a requirement
Ability to write production-quality, maintainable code beyond model scripts
Experience working Agile with cross-functional, client-facing teams
About MAQR
MAQR is the internal ML, AI, and quantitative research function of TWG Global AI, a subsidiary of TWG Global Holdings, operating in joint venture with Palantir. Our client portfolio spans asset management, financial services, insurance, marketplace platforms, and field services operations. We build bespoke data science solutions tailored to each engagement — often across multiple industries simultaneously.
If you want a role where the scope changes fast, the problems are genuinely hard, and getting things into production is the scoreboard, MAQR is the right team. We value craft, intellectual honesty, and delivery.
Benefits
Position Location
This position is located in Santa Monica, CA.
Compensation
The base pay for this position is $190,000-200,000. A bonus will be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits.
TWG is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development.
You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation.
At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses.
The Role
Join our growing Machine Learning, AI & Quantitative Research (MAQR) group as a Senior Data Scientist. MAQR is a small, high-ownership team embedded across a diverse portfolio of businesses — from financial services and insurance to marketplace trading platforms and operations. We don't have a narrow mandate. We build what needs to be built, ship it to production, and measure what matters.
This role is for a data scientist who is as comfortable wrangling a messy operational dataset as they are designing a production inference pipeline. You bring rigor without rigidity — you know when a logistic regression is the right answer and when it isn't. You've built things that run in the real world, not just in notebooks.
We operate with a startup mindset inside a well-capitalized holding company. You will have real ownership, real clients, and real impact.
Key Responsibilities:
Algorithm to Production
Own the full model lifecycle: from problem formulation and feature engineering through training, validation, and deployment in a live production environment
Build and maintain ML pipelines that run reliably — not just models that score well offline
Instrument models for monitoring, drift detection, and retraining triggers in production systems
Work closely with engineers to integrate models into product workflows, operational tooling, and client-facing applications
Applied Science & Experimentation
Develop predictive models, time series forecasts, anomaly detection systems, and causal inference frameworks across varied business contexts
Design and analyze experiments — from A/B tests to geo-randomized trials — with proper statistical rigor
Translate ambiguous business problems into well-scoped data science problems; define success metrics before modeling begins
Review relevant literature and apply state-of-the-art methods where warranted; resist overengineering where simpler approaches perform just as well
Collaboration & Communication
Partner with cross-functional teams — business leads, engineers, product managers, and analysts — to deliver solutions aligned with real objectives
Translate model outputs and technical trade-offs into clear, accessible communication for business stakeholders
Contribute to AI governance standards and champion responsible, explainable AI practices across client engagements
Requirements
Master's degree in a quantitative field (Statistics, Computer Science, Mathematics, Operations Research, Engineering, or related) plus 6+ years of applied data science experience, OR PhD in a quantitative field plus 3+ years of hands-on industry experience
At least 6+ years of Python (or equivalent) for data analysis, model development, and production-ready scripting
At least 4+ years of end-to-end ML model development and deployment — not just training, but shipping to production
Demonstrated fluency across the data science stack: wrangling, feature engineering, modeling, evaluation, and deployment tooling
Strong statistical foundations: hypothesis testing, regression, Bayesian reasoning, model validation, and uncertainty quantification
Proven ability to bring definition and structure to large, ambiguous problems
Clear communicator who can explain model trade-offs to an engineer and business impact to an executive
Preferred qualifications:
Experience with cloud platforms and cloud-native ML workflows
MLOps familiarity: pipeline orchestration, model registries, CI/CD for ML, and inference infrastructure
Experience with large-scale or real-time data (streaming, event-driven, high-volume batch processing)
Background in time series forecasting, causal inference, or combinatorial optimization
Experience in financial services, insurance, marketplace integrity, or operations-heavy industries
Knowledge of quantitative finance, econometrics, or portfolio methods — useful context, not a requirement
Ability to write production-quality, maintainable code beyond model scripts
Experience working Agile with cross-functional, client-facing teams
About MAQR
MAQR is the internal ML, AI, and quantitative research function of TWG Global AI, a subsidiary of TWG Global Holdings, operating in joint venture with Palantir. Our client portfolio spans asset management, financial services, insurance, marketplace platforms, and field services operations. We build bespoke data science solutions tailored to each engagement — often across multiple industries simultaneously.
If you want a role where the scope changes fast, the problems are genuinely hard, and getting things into production is the scoreboard, MAQR is the right team. We value craft, intellectual honesty, and delivery.
Benefits
Position Location
This position is located in Santa Monica, CA.
Compensation
The base pay for this position is $190,000-200,000. A bonus will be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits.
TWG is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.