Data Scientist(6 Months Contractual)

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 (flexible for exceptional talent).