Data Scientist / Data Engineer

Title: Data Scientist / Data Engineer

Location: Pune, India

Employment Type: This is a full time position with Insight Global in Pune, supporting a major Fortune 500 organization. It follows a contract to hire model with potential for client conversion.

Salary: 23-27 LPA

Required Skills & Experience

- 3+ years of experience as a Data Scientist / Data Engineer

- Strong python-based ML experience

- Experience designing, building, and maintaining data processing and ETL pipelines using Python and SQL.

- Experience performing data cleaning, scrubbing, and standardization across large legacy datasets.

- Experience leverage Databricks and Azure data platforms to support scalable data processing.

-Strong Python programming experience

-Hands‑on experience building data pipelines and ETL workflows.

-Strong SQL skills; PostgreSQL experience preferred.

-Experience parsing and working with Excel and JSON data.

-Proven experience with data cleaning, normalization, and pre‑processing.

-Experience working with legacy, inconsistent, or unstructured datasets.

Job Description

We are seeking a Data Scientist / Data Engineer to support a high‑impact internal engineering modernization initiative. This role will focus heavily on data pre‑processing, cleaning, and transformation of large volumes of complex legacy data, with future phases expanding into machine learning and AI‑driven capabilities.

The individual will work on automating a historically manual, engineering‑driven process by transforming years of semi‑structured data into a scalable, searchable solution supported by a user interface. This role is ideal for someone who enjoys working with messy, real‑world data, building robust pipelines, and contributing to systems that directly support engineers and customers.

The project centers around approximately 2,100+ Excel files used by engineering teams to configure highly specialized customer products. These files contain 10–15 years of historical engineering configurations, with very specific values that vary depending on application and customer needs.

Today, engineers manually search across hundreds or thousands of files to find similar configurations. The goal of this initiative is to automate that process by enabling engineers to input a small set of parameters into a custom internal UI, which will then surface relevant historical configurations.

Initial UI work has already been completed internally. This role will help advance the project by strengthening the data foundation and enabling future AI/ML‑based similarity matching and intelligence.