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