Sr. Data Scientist

Analytic Partners•New York, NY

1d•$95,000 - $120,000

About The Position

Analytic Partners is a global leader in commercial measurement and optimization, turning data into expertise for the world’s largest brands for almost 25 years. With clients in 50+ countries and global offices across New York City, Miami, Dallas, Dublin, London, Paris, Singapore, Shanghai, Munich, Sydney, Melbourne, Charlottesville and Denver, we’re growing fast. And we’re looking for top talent to join us in shaping the future of analytics. To learn more about what we do, visit analyticpartners.com – and see why we’re recognized as a Leader in the industry by independent research firms Forrester and Gartner . POSITION & TEAM OVERVIEW In this role as Senior Data Scientist, you will be a key technical contributor on the Science Team, responsible for designing, developing, and deploying advanced data science and AI solutions that power Analytic Partners’ products and client innovations. You will leverage statistical modeling, machine learning, and Generative AI to solve complex business problems and drive measurable impact across AP platforms and products. The Science Team is an integral part of the R&D Group, building science and technology applications that feed the analytical engines across AP. The team is comprised of highly motivated individuals who do exceptional work, tackle tough challenges together, and continuously raise the bar for analytics and AI excellence. You will collaborate closely with Product, Engineering, and fellow Data Scientists to architect, implement, test, and continuously deliver new models, features, and AI-enabled capabilities to the company and our clients.

Requirements

Advanced degree (MS) in Computer Science, Statistics, Engineering, Economics, Applied Mathematics, or a related quantitative field.
6–8+ years of industry experience in data science, applied machine learning, or related quantitative roles.
Strong hands-on experience building production-grade ML systems, from prototyping through deployment and iteration.
Deep experience with NLP and Generative AI, including:
LLM-based applications
Prompt engineering and evaluation
Fine-tuning or adapting LLMs
Familiarity with open-source LLM ecosystems and tooling
Solid foundation in statistics and modeling, with the ability to reason about model assumptions, validation, and trade-offs.
Passion for applying cutting-edge AI/ML techniques to real-world business problems.
Strong programming skills (e.g., Python) and experience working with data pipelines, version control, and modern ML tooling.

Nice To Haves

A strong business mindset, with the ability to translate complex analysis and technical concepts into clear, actionable insights for partners and leadership.
Excellent communication skills and the ability to collaborate cross-functionally with Product, Engineering, and Science leadership.
Self-driven, curious, and comfortable navigating ambiguity in a fast-moving R&D environment.

Responsibilities

Design, develop, and deploy advanced analytical and machine learning solutions, including econometric, statistical, and AI/ML-based approaches, to support AP products and custom solutions.
Build and enhance Generative AI and LLM-based applications, working closely with Engineering partners to productionize scalable, reliable AI-enabled systems.
Drive end-to-end model development, from problem formulation and data exploration through modeling, validation, deployment, and monitoring.
Partner with Product, CET, and business stakeholders to translate business needs into clear analytical and AI-driven solutions.
Evaluate and experiment with new data sources, modeling techniques, and AI technologies to improve existing solutions and unlock new product opportunities.
Contribute to best practices in model quality, explainability, reproducibility, and deployment across the Science Team.
Mentor and collaborate with other Data Scientists by sharing knowledge, reviewing work, and helping tackle technically challenging problems.
Clearly communicate analytical insights, model behavior, and technical trade-offs to both technical and non-technical audiences.