Lead / Manager, Data Scientist

Role Overview

Blend is a premier AI services provider that combines human expertise with artificial intelligence to create meaningful impact for its clients. The company is dedicated to unlocking value and fostering innovation through data-driven strategy. The ideal candidate should have a strong foundation in statistics, machine learning, predictive modelling, and optimization, with hands-on experience in Python, SQL, and cloud-based machine learning environments.

What You Will Do

The role combines hands-on technical expertise with data science leadership, requiring the ability to lead and mentor data scientists, solve complex business problems, engage with clients, and translate analytical insights into measurable business outcomes. Key responsibilities include leading and mentoring a team of data scientists, developing and delivering data science solutions, and applying model explainability techniques.

Why It Might Be a Fit

The ideal candidate should have 9+ years of hands-on experience in Data Science, Advanced Analytics, or a related quantitative field, with experience leading or mentoring data scientists. Experience in LLM and Agentic AI Engineering Systems, strong programming skills in Python, and strong SQL skills are highly regarded.

Requirements

• 9+ years of hands-on experience in Data Science, Advanced Analytics, or a related quantitative field
• Experience in LLM and Agentic AI Engineering Systems
• Strong programming skills in Python
• Strong SQL skills
• Strong understanding of applied statistics, probability, regression, hypothesis testing, model evaluation, and statistical inference
• Hands-on experience with Bayesian Modelling & Optimization, K-Means, Gaussian Mixture Models (GMM), DBSCAN, Regression Modelling, Random Forest, Decision Trees, Support Vector Machines (SVM), and other machine learning techniques
• Strong experience in feature engineering, model selection, validation, tuning, and performance evaluation
• Experience with SHAP or other model explainability techniques
• Experience with pricing optimization, price elasticity, revenue optimization, or related decision science problems
• Experience with Marketing Mix Modelling (MMM), adstock, saturation, response curves, and media budget optimization
• Experience with A/B testing, experimental design, causal inference, geo experiments, Difference-in-Differences, Synthetic Control, or Regression Discontinuity
• Hands-on experience with AWS, particularly AWS SageMaker for model development, deployment, monitoring, or experimentation
• Experience in financial services, retail, media, marketing, loyalty, payments, or customer analytics

Benefits

• Competitive Salary
• Dynamic Career Growth
• Idea Tanks
• Growth Chats
• Snack Zone
• Recognition & Rewards
• Fuel Your Growth Journey with Certifications