Lead Data Scientist

Weekday AI (YC W21); Maharashtra; On-site; INR 1,600,000 - 2,500,000; Full time

Job summary

A prominent tech company is looking for a Data Science Lead to drive end-to-end projects and mentor a data science team. This role entails designing scalable pipelines, guiding model development, and effectively communicating insights to stakeholders. Candidates should have 4-5 years of experience, a background in statistics and optimization, strong proficiency in Python and ML libraries, and familiarity with cloud ecosystems. The position offers a full-time opportunity based in Mumbai, with a competitive salary ranging from INR 16-25 LPA.

Qualifications

• 4-5 years of hands-on experience in Data Science or Machine Learning.
• Strong theoretical foundation in statistics, linear algebra, and optimization.
• Solid expertise in Python and popular libraries such as NumPy, pandas, scikit-learn, and XGBoost.

Responsibilities

• Drive the complete lifecycle of data science projects—from problem definition to deployment and monitoring.
• Convert business requirements into structured DS tasks, identifying suitable modeling approaches.
• Lead data preprocessing, feature engineering, and model optimization for strong performance.

Skills

Data Science

Machine Learning

Python

Data Pipelines

Statistical Analysis

Communication

Education

Bachelor's degree in any discipline

Tools

Docker

AWS

GCP

Azure

Airflow

Spark

FastAPI

Job description

This range is provided by Weekday AI (YC W21). Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base Pay Range

• Job for one of Weekday's clients
• Salary range: Rs 1600000 - Rs 2500000 (INR 16-25 LPA)
• Minimum Experience: 1 year
• Location: Mumbai
• Job Type: Full-time

This role involves leading end-to-end data science initiatives—right from requirement scoping and dataset preparation to model development, deployment, and measurable business impact. You will translate business challenges into structured analytical problems, design scalable data pipelines, and build robust ML solutions. The position also requires guiding a mid-sized data science team, ensuring delivery efficiency, quality, and clarity in communicating insights across technical and non-technical stakeholders. You will play a key role in establishing reusable MLOps frameworks, automated workflows, and experimentation processes that accelerate innovation.

Key Responsibilities

• Drive the complete lifecycle of data science projects—from problem definition to deployment and monitoring
• Convert business requirements into structured DS tasks, identifying suitable modeling approaches
• Lead data preprocessing, feature engineering, and model optimization for strong performance
• Communicate insights clearly to diverse stakeholders and support strategic decision-making
• Mentor and guide a team of data scientists to improve skills, output, and analytical thinking
• Build scalable data pipelines using modern orchestration tools
• Implement efficient MLOps practices, automated workflows, CI/CD, and reusable DS frameworks
• Ensure models are deployed reliably using containerization and API frameworks
• Foster a culture of experimentation, iteration, and high-quality analytical rigor

What Makes You a Great Fit

• 4-5 years of hands‑on experience in Data Science or Machine Learning
• Strong theoretical foundation in statistics, linear algebra, and optimization
• Solid expertise in Python and popular libraries such as NumPy, pandas, scikit-learn, and XGBoost
• Experience working on at least one cloud ecosystem—AWS, GCP, or Azure
• Skilled in building and managing data pipelines using tools like Airflow or Spark
• Proficient in deploying ML models using Docker, FastAPI, or similar frameworks
• Excellent communication skills with the ability to simplify complex insights
• Bachelor's degree in any discipline
• Added advantage if you have worked with LLMs, built GenAI applications, contributed to open-source, or handled real-time analytics
• Thrive in fast-paced, iterative, agile environments and enjoy taking ownership
• Comfortable working with high-performing teams and navigating startup-level unpredictability