Data Scientist

VenPep Group; Coimbatore; On-site; INR 900,000 - 1,500,000; Full time

Job summary

VenPep Group is seeking data science professionals to build and deploy ML models for marketing, including audience segmentation and churn prediction, with robust experiment design and clear stakeholder communication.

The role spans data exploration, feature engineering, model evaluation, and collaboration with engineering to expose models via APIs, while ensuring privacy-by-design and rigor in analysis.

Qualifications

• 2–3 years of hands-on data science or applied ML experience in a product or client-delivery environment.

Responsibilities

• Build, validate and deploy ML models for marketing use cases: audience segmentation, lead scoring, churn prediction, LTV estimation, forecasting, attribution.
• Design and analyse A/B and multivariate experiments with hypotheses, sample sizes, bias control and confidence intervals.
• Perform exploratory analysis on large marketing/web datasets and translate findings into clear recommendations for clients and internal stakeholders.
• Partner with engineering to expose models as APIs and with delivery teams to interpret results for clients.

Skills

Python

Pandas

NumPy

Scikit-learn

SQL

Tableau

Power BI

Statistical modeling

Education

Bachelor’s or Master’s in Computer Science/Statistics/Data Science

Tools

XGBoost

LightGBM

LLM APIs

Job description

Analytics & Modelling

• Build, validate and deploy ML models for marketing use cases: audience segmentation, lead scoring and propensity, churn prediction, LTV estimation, demand/spend forecasting, and campaign attribution.
• Design and analyse A/B and multivariate experiments — define hypotheses, compute sample sizes, control for bias, and state results with confidence intervals rather than point estimates.
• Perform exploratory analysis on large marketing and web datasets; translate findings into clear recommendations for client and internal stakeholders.

Generative AI & RAG

• Build and improve retrieval-augmented generation pipelines: document ingestion, chunking strategy, embedding generation, vector storage and retrieval evaluation.
• Develop and tune prompts, evaluation harnesses and guardrails for LLM-backed assistants; measure quality with objective metrics (groundedness, retrieval precision/recall, latency, cost per request).
• Contribute to agentic and tool-calling workflows where LLMs invoke internal APIs and business tools.

Data & Engineering

• Write production-grade Python; build reproducible feature pipelines and transformations over the lakehouse/warehouse.
• Own the full model lifecycle — versioning, offline/online evaluation, deployment, monitoring and retraining triggers; detect and respond to data and concept drift.
• Produce clear documentation: data dictionaries, model cards, assumptions, limitations and evaluation results.

Collaboration & Governance

• Partner with engineering to expose models as APIs and with delivery teams to interpret results for clients.
• Apply privacy-by-design: PII minimisation, anonymisation/pseudonymisation, and handling of client data consistent with India’s DPDP Act and Singapore’s PDPA (several clients are Singapore-based).
• Never fabricate business numbers — cite data sources and state uncertainty explicitly.

Required Qualifications

• 2–3 years of hands-on data science or applied ML experience in a product or client-delivery environment (internships and academic projects do not substitute for production experience).
• Bachelor’s or Master’s in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Python — strong proficiency with pandas, NumPy, scikit-learn; clean, testable, modular code (not notebook-only).
• SQL — comfortable with joins, window functions, CTEs and query optimisation over large tables.
• Solid grounding in statistics: hypothesis testing, regression, resampling, bias/variance, and the difference between correlation and causation.
• Practical experience with at least one of: gradient boosting (XGBoost/LightGBM), time-series forecasting, or NLP.
• Demonstrated experience with LLM APIs or frameworks — prompt design, embeddings, or a RAG implementation taken beyond a demo.
• Version control with Git and collaborative PR-based workflows.
• Clear written and spoken English; able to explain a model’s behaviour to a non-technical stakeholder.

Preferred (Good To Have)

• AWS experience — S3, Lambda, Bedrock, SageMaker, Athena, or Aurora PostgreSQL.
• Vector search in production — pgvector, OpenSearch, or similar, including retrieval evaluation.
• Spark / Databricks or equivalent distributed processing.
• MLOps tooling: MLflow, model registries, CI/CD for ML, containerisation.
• Marketing/adtech domain exposure — GA4, Meta/Google Ads data, HubSpot or other CRM data models, attribution modelling.
• Dashboarding and storytelling: Power BI, Streamlit, or similar.
• Open-source contributions, Kaggle results, or a public portfoli

Skills: machine learning,sql,tableau,power bi,scikit-learn,python,pandas,statistical modeling