Senior Data Scientist

Sapwood Ventures; Dadri; Hybrid; INR 450,000 - 900,000; Full time

Job summary

Sapwood Ventures is seeking a Data Scientist to own the analytical and modelling lifecycle from framing a business question to a deployed insight. You will collaborate with product, business and engineering teams, design experiments, and build models that influence decisions while guiding responsible AI practices.

Responsibilities include full analytical lifecycle, model development and deployment, experimentation, data pipeline contributions, and stakeholder communication.

Qualifications

• Bachelor's degree required; Master's in data science, statistics, ML, or equivalent preferred.
• 8–12 years as a Data Scientist or in an equivalent ML role with end-to-end ownership.
• Strong Python (Pandas, NumPy, Scikit-learn); working knowledge of XGBoost/LightGBM and TF/PyTorch.
• SQL with window functions.
• Experience with MLflow, Weights & Biases, or similar; basic cloud platform familiarity.
• Solid hypothesis testing, regression and experimental design; communicate uncertainty clearly.
• Track record of insights that influenced product or business decisions.
• AI literacy; uses AI tools across the modelling lifecycle.

Responsibilities

• Own the full analytical lifecycle from framing to deployed insights.
• Design, build, and deploy predictive/ML models into production or decision workflows.
• Own A/B test design, execution, and analysis and report significance to stakeholders.
• Partner with Data Engineers to define data requirements and feature pipelines.
• Translate business questions into analytical problems and present findings to technical and non-technical audiences.
• Champion data quality, governance and responsible AI use; mentor junior scientists.

Skills

Python

SQL

Pandas

NumPy

Scikit-learn

XGBoost

LightGBM

TensorFlow

PyTorch

AB testing

Experiment design

Education

Bachelor's degree

Master's degree

Tools

MLflow

Weights & Biases

Job description

Data Scientist

Location: Noida

Employment Type: Full-time

Work Mode: Hybrid | 2 - 3 days working from the office per week

Experience Required: 8 - 12 Years

About the Role

We are looking for a Data Scientist to own the analytical and modelling lifecycle from framing an ambiguous business question through to a deployed decision-ready insight. You will work closely with product, business and engineering teams, design and analyse experiments, and build predictive models that influence real decisions. You will also help shape how our team uses AI Tools responsibly across the modelling workflow.

Key Responsibilities

Statistical & Quantitative Analysis: own the full analytical lifecycle, including preprocessing EDA, and statistical modelling, and deliver insights framed in business terms.

Model Development & Deployment: Design, build, and deploy predictive and ML models (regression, classification, clustering, time-series) into production or decision-support workflows owning evaluation, validation, and iterative improvement.

Experimentation: Own A/B test design, execution, and analysis, and communicate statistical significance and uncertainty clearly to product and business stakeholders.

Data Pipeline Contribution: Partner with Data Engineers to define data requirements and feature pipelines, and build or maintain analytical datasets and feature stores.

Stakeholder Collaboration: Translate ambiguous business questions into well-defined analytical problems and present findings to both technical and non- technical audiences.

Data Quality, Governance & AI Enablement: Champion data quality, apply governance best practices, and mentor junior Scientist or responsible AI use.

Required Skills & Qualifications

Bachelor's degree required: Master's in Data Science, Statistics, ML, or equivalent preferred.

8 - 12 Years as a Data Scientist or in an equivalent ML role, with end-to-end ownership of modelling and experimentation workflows.

Strong Python (Pandas, NumPy, Scikit-learn); working knowledge of XGBoost/LightGBM and Tensorflow/Pytorch.

Sql, including window functions.

Experience with MLflow, W & B, or similar; basic cloud platform familiarity.

Solid hypothesis testing, regression, and experimental design; familiarity with casual interface (DiD, regression discontinuity).

Ability to communicate statistical uncertainty to non-technical stakeholders.

Track record of insights that directly influenced product or business decisions.

AI Literacy

Uses AI tools across the modelling lifecycle (EDA, feature engineering, development, communication).

Applies advanced iterative prompting and structured governance to AI generated outputs.

Mentors junior scientists or responsible AI use.

Technology Stack

Languages: Python, SQL

Libraries: Pandas, Numpy, Scikit-learn

ML Frameworks: XGBoost, LightGBM, TensorFlow/PyTorch

Experiment Tracking: MLflow, Weights & Biases (or similar)

Cloud: AWS, Azure, or GCP

AI Tools: LLM assistants and code copilots