Data Scientist

Aditya Birla Management Corporation (ABMCPL); Bengaluru; On-site; INR 1,000,000 - 1,800,000; Full time

Job summary

Aditya Birla Management Corporation (ABMCPL) is seeking a Data Scientist in Bengaluru to translate business needs into robust data science solutions. The role emphasizes lifecycle management, data quality, and scalable analytical workflows that drive innovation.

The candidate will work with cutting-edge ML methods, ensure reproducibility, and collaborate with engineering teams to deploy AI solutions in production, while continuously improving models via experimentation and stakeholder feedback.

Qualifications

• Proven ability to apply advanced ML techniques (e.g., ANN, XGBoost, deep learning).
• Experience implementing AI frameworks and best practices across model lifecycle.

Responsibilities

• Develop and apply cutting-edge machine learning techniques to solve business problems.
• Oversee end-to-end AI project lifecycle from data prep to deployment and monitoring.
• Collaborate with engineering teams to integrate AI solutions into production environments.
• Use JIRA and other tools to track tasks, deadlines, and progress of AI initiatives.
• Maintain structured model versioning, documentation, and reproducibility.

Job description

Position Title - Data Scientist

Job Location - Bengaluru

Job Purpose

The role focuses on implementing data science solutions and best practices while managing their lifecycle within a business unit. It involves translating business needs into technical solutions, ensuring data quality, and optimizing analytical workflows to drive innovation and efficiency

Principal Accountabilities

Advanced Data Science & AI Implementation

• Develop and apply cutting-edge machine learning techniques such as ANN, XGBoost, and deep learning models to solve complex business problems.
• Ensure the use of state-of-the-art AI frameworks and methodologies to enhance model accuracy, efficiency, and scalability.
• Optimize AI models by fine-tuning hyperparameters, feature engineering, and advanced algorithm selection to maximize performance.

End-to-End Solution Development

• Oversee the full AI project lifecycle, from data preparation and model development to deployment and monitoring.
• Ensure smooth integration of AI solutions into production environments, collaborating with engineering teams for scalable deployments.
• Use project management tools like JIRA to track tasks, deadlines, and progress, ensuring structured execution of AI initiatives.
• Maintain a structured framework for model versioning, documentation, and reproducibility to ensure long-term sustainability.

Experimentation & Continuous Improvement

• Implement a systematic experimentation approach to identify gaps in current AI models and refine them iteratively.
• Conduct A/B testing, error analysis, and post-deployment monitoring to assess model effectiveness and business impact.
• Drive an innovation-first mindset, experimenting with new algorithms and methodologies to improve existing solutions.
• Establish a feedback loop between model performance and business outcomes, ensuring AI-driven insights remain relevant and impactful.

Technical Leadership & Best Practices

• Enforce best practices in coding, model documentation, and version control to enhance standardization across AI initiatives.
• Ensure teams stay updated with emerging AI/ML methodologies and provide structured guidance for integrating new techniques.
• Act as a mentor to junior data scientists, providing hands-on guidance while establishing technical excellence within the team.

Cross-Team Collaboration & Stakeholder Management

• Ensure seamless integration of AI solutions into business workflows.
• Lead discussions with stakeholders to align AI initiatives with operational priorities and measurable outcomes.
• Standardize communication channels to ensure clarity, accountability, and alignment across teams.

Scalable AI Solutions & Reusability

• Drive the standardization of AI solutions to enable their scalability and adaptability across multiple business functions.
• Develop reusable AI frameworks, ensuring consistency and reducing redundancy in data science efforts.
• Establish best practices for documenting and sharing AI learnings across teams, improving operational efficiency and knowledge transfer. Business KRA (as applicable) (Add Rows as needed)

Non-Monetary

• Data & AI driven improvement in KPIs relevant to a particular business or initiative such as quality, CX, stability, risk, efficiency

Decision making & augmentation

• Decisions replaced or augmented with the use of data & AI
• Decisions improved through the use of data & AI

AI Capability

• Drive, through examples, the data driven decision making culture

*********************Job will expire after 30th Sept 2026******************