Lead / Manager, Data Scientist

About Blend

Blend is a leading AI services firm focused on generating significant client impact by integrating data science, AI, technology, and human expertise. Our mission is to realize bold client visions by blending advanced analytics with skilled professionals, fostering innovation and unlocking value. Our approach harnesses world-class talent and data-driven strategies to create meaningful changes in workplaces and projects.

Role Summary

We seek an accomplished Data Science Manager to guide the creation and execution of sophisticated analytics solutions encompassing pricing optimization, customer segmentation, marketing analytics, Bayesian methods, marketing mix modeling (MMM), causal inference, and optimization techniques.

This position demands a blend of hands-on technical skill and leadership capability to mentor data scientists, address complex business challenges, engage with clients, and convert analytics outputs into tangible business benefits.

Responsibilities

• Provide leadership and mentorship to data science teams across various projects, ensuring technical excellence.
• Own full lifecycle delivery of data science initiatives, including problem framing, exploratory data analysis, modeling, validation, deployment, and adoption.
• Develop and manage pricing optimization and price elasticity models that enhance revenue, profit margins, and market position.
• Build customer segmentation and targeting models leveraging algorithms such as K-Means, Gaussian Mixture Models (GMM), and DBSCAN.
• Create predictive and statistical models using methods like Regression, Random Forests, Decision Trees, and Support Vector Machines (SVM).
• Construct Bayesian regression and hierarchical Bayesian models utilizing PyMC, Stan, or similar frameworks.
• Utilize causal inference and experimental design techniques including A/B testing, geo experiments, Difference-in-Differences, Synthetic Control, and Regression Discontinuity designs.
• Design constrained and multi-objective optimization algorithms for pricing, media allocation, and other business decisions using SciPy Optimize, CVXPY, and related tools.
• Deploy and operationalize machine learning solutions on cloud platforms like AWS SageMaker.
• Implement model interpretability methods such as SHAP to convert complex model results into actionable business insights.
• Collaborate closely with clients and senior leaders to understand challenges, communicate findings, and drive data science-based business recommendations.
• Establish best practices in model development, experimentation, documentation, version control, reproducibility, and governance.
• Review team-generated models and analytics to guarantee statistical robustness and technical quality.
• Proactively identify and pursue business opportunities where advanced analytics can add measurable value.

Qualifications and Experience

• Over 9 years of direct experience in Data Science or quantitative analytics fields, including leadership or mentoring of data scientists.
• Preferred expertise in Large Language Models (LLM) and Agentic AI engineering systems with proven delivery and client engagement.
• Proficient programming skills in Python, including libraries such as pandas, NumPy, scikit-learn.
• Strong SQL knowledge encompassing joins, window functions, aggregations, and handling large datasets.
• Deep understanding of applied statistics, probability, regression analysis, hypothesis testing, and model evaluation techniques.
• Hands-on experience with Bayesian modeling, clustering methods (K-Means, GMM, DBSCAN), and supervised/unsupervised machine learning techniques.
• Skilled in feature engineering, model selection, validation, hyperparameter tuning, and performance assessment.
• Experience with model interpretability tools like SHAP.
• Working knowledge of pricing optimization, price elasticity analysis, revenue optimization, or decision science challenges.
• Familiar with marketing mix modeling concepts including adstock, saturation, response curves, and media budget optimization.
• Experienced in experimental design and causal inference methods such as A/B testing, geo experiments, Difference-in-Differences, Synthetic Control, and Regression Discontinuity.
• Ability to interpret causal effects, quantify uncertainties, and translate experimental outcomes into actionable business strategies.
• Hands-on with cloud technologies, especially AWS SageMaker for machine learning model development, deployment, and monitoring.
• Preferred background in domains such as financial services, retail, media, marketing, loyalty programs, payments, or customer analytics.
• Educational qualification: Master’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, or other quantitative fields is preferred.

Additional Information

• Competitive remuneration reflecting your expertise and contribution.
• Opportunities for rapid career advancement supported by mentorship and professional growth initiatives.
• Innovation is encouraged through "Idea Tanks," collaborative sessions to pitch and pilot new concepts.
• Engage in informal "Growth Chats" with peers and experts to continuously elevate your skills.
• Access to complimentary snacks to stay energized and inspired.
• Recognition programs including peer acknowledgments and rewards celebrating impactful work.
• Company-sponsored certifications in AI, Data Science, Cloud, and Analytics to enhance your professional capabilities.