Subcon - Data Scientist & AIML

Area(s) of responsibility

Empowered By Innovation
Birlasoft, a global leader at the forefront of Cloud, AI, and Digital technologies, seamlessly blends domain expertise with enterprise solutions. The company’s consultative and design-thinking approach empowers societies worldwide, enhancing the efficiency and productivity of businesses. As part of the multibillion-dollar diversified CKA Birla Group, Birlasoft with its 12,000+ professionals, is committed to continuing the Group’s 170-year heritage of building sustainable communities.

Job Title / Position: Data Scientist AI & ML (contractual)
Experience: 6-8 years
Location: Pune

Role Purpose
The Data Scientist leverages advanced analytics, statistics, machine learning, and AI techniques to transform data into actionable business insights. The role partners closely with business stakeholders to solve complex problems, improve decision-making, and drive measurable business outcomes across functions such as manufacturing, supply chain, commercial operations, and predictive maintenance.
Key Responsibilities
•    Develop predictive, statistical, and machine learning models to address business challenges.
•    Perform exploratory data analysis to identify trends, risks, and opportunities.
•    Prepare, clean, and transform structured and unstructured data for analytics.
•    Translate business requirements into analytical solutions and decision-support models.
•    Communicate analytical findings and recommendations to technical and business audiences.
•    Evaluate emerging AI/ML technologies and contribute to analytics innovation.
•    Collaborate with data engineers, architects, and business SMEs to ensure successful solution delivery.
•    Promote data science best practices and continuous improvement across the organization.
Core Skills & Technologies
•    Python, R, SQL
•    Machine Learning & Artificial Intelligence
•    Statistical Modeling & Predictive Analytics
•    Data Visualization (Power BI)
•    Feature Engineering & Data Mining
•    Data platform (Snowflake and Databricks)
•    Cloud Analytics Platforms (Azure, AWS, GCP, Snowflake)
Success Measures
•    Delivery of actionable business insights
•    Accuracy and effectiveness of predictive models
•    Adoption of analytics solutions by business users
•    Quantifiable business value generated through data-driven initiatives.