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; 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
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.
2–3 years of hands-on data science or applied ML experience in a product or client-delivery workplace (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; 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.
- Clear written and spoken English; able to explain a model’s behaviour to a non-technical stakeholder.

AWS experience — S3, Lambda, Bedrock, SageMaker, Athena, or Aurora PostgreSQL.
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.
Power BI, Streamlit, or similar.

📌 Data Scientist (Python, ML, SQL) (Coimbatore)
🏢 venpep Solutions
📍 Coimbatore