Data Scientist

Sapwood Ventures; Dadri; Hybrid; INR 1,200,000 - 2,000,000; Full time

Job summary

Sapwood Ventures is seeking a Data Scientist in Noida for a full-time, hybrid role. You will own the full modelling lifecycle, from framing business questions to deployment, collaborating with product, business, and engineering teams.

Expect to design experiments, develop predictive models, and contribute to responsible AI practices. You will work on data pipelines, feature stores, and communicate results to both technical and non-technical stakeholders.

Qualifications

• Bachelor's degree required in a quantitative field.
• 4–8 years experience as a data scientist or similar ML role with end-to-end modelling ownership.
• Strong Python (Pandas, NumPy, Scikit-learn) and SQL skills.
• Experience with ML lifecycle tools (MLflow, W&B) and cloud platforms.
• Excellent ability to communicate statistical concepts to non-technical stakeholders.

Responsibilities

• Own the analytical and modelling lifecycle from framing questions to deployed insights.
• Design, build, and deploy predictive and ML models (regression, classification, clustering, time-series).
• Lead A/B test design, execution, and analysis; communicate significance clearly.
• Collaborate with data engineers to define data requirements and feature pipelines.
• Mentor junior scientists and promote responsible AI practices.

Skills

Python

SQL

Statistical analysis

Experiment design

Communication

Education

Bachelor's degree required

Master's in Data Science/Statistics/ML preferred

Tools

MLflow

Weights & Biases

Cloud platforms (AWS/Azure/GCP)

TensorFlow/PyTorch

XGBoost/LightGBM

Job description

Data Scientist

Location: Noida

Employment Type: Full-time

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

Experience Required: 4 - 8 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.

4 - 8 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