Role & responsibilities

- Raw Data Extraction & Parsing
- Build automated workflows to extract data from Excel (multi-tab, inconsistent formats), PDFs (native & scanned), BOQs, engineering docs, emails, and semistructured project files.
- Apply OCR, document intelligence, and LLM-based extraction to interpret non-standard formats.
- Create resilient pipelines for noisy, incomplete, and variable datasets.

- Data Cleaning, Normalisation & Standardisation

- Define rules for data consistency, missing values, deduplication, units/convention standardisation.
- Normalise data across regions, sectors, and business units.
- Convert raw inputs into validated, analysis-ready datasets.

- Data Transformation & Template Building

- Build summarised, structured templates (mapping rules, classification logic, grouping/segmentation, feature engineering).
- Automate generation of consistent output from multi-source inputs; evolve templates over time.

- LLM & Prompt Engineering

- Use LLMs to interpret complex documents, extract insights, classify and map data.
- Develop prompt libraries, reusable chains, and guardrails for accuracy/reproducibility.

- Machine Learning Model Development

- Develop models across regression, boosting, time-series forecasting, clustering/similarity,



and NLP where relevant.
- Perform feature engineering, hyperparameter tuning, crossvalidation, and model interpretation.

- Pipelines, Automation & MLOps Awareness

- Build end-to-end workflows (extraction cleaning standardisation transformation modelling).
- Write modular, productionready code and integrate with CI/CD where applicable.

- Documentation & Governance

- Maintain documentation for schemas, extraction logic, models, prompts, and validation rules.
- Ensure reproducibility, version control, and code quality (PEP8 or equivalent).

- Collaboration

- Work with Digital & Data, domain experts (costing/engineering/PM), and IT infra teams.
- Translate domain rules and business logic into robust data/ML implementations.

Preferred candidate profile

- Background in SCM, procurement, logistics, construction/engineering, or industrial/manufacturing datasets.
- Comfort dealing with regional variance and non-standard data schemas.
- Bachelors/Masters in Data Science, Computer Science, Statistics, Engineering, Mathematics, or related fields.
- 3–7 years of relevant experience (adaptable for exceptional talent).

📌 Data Scientist(6 Months Contractual) (Mumbai)
🏢 WTP Cost Management
📍 Mumbai