Junior Data Scientist

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Junior Data Scientist

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Data Science Bengaluru

Full Time

2 - 3 Years

On Site

About TWID

TWID is a Bengaluru-based fintech, founded in 2020, building an AI-powered Incentive Commerce Network.

TWID started by solving the problem of fragmented reward points, enabling consumers to make better use of rewards across merchants. The company then evolved into Pay with Rewards and is now building the next phase of its platform around AI, transaction intelligence and incentive commerce.

TWID operates at the intersection of FinTech, Payments, AI, Data and Commerce, connecting consumers, issuers, fintechs, merchants and brands.

Today, TWID has 5,000+ merchants, 30+ issuer partners and 1B+ monthly transaction signals, creating a large and diverse data environment for solving real-world customer and business problems.

About the Role

We are looking for a Data Scientist with strong foundations in machine learning, experimentation, and data analytics, who can take models from development to production and translate business problems into measurable ML solutions.

This role will work closely with data and business problems emerging from TWID's transaction intelligence and incentive commerce ecosystem.

Key Responsibilities

• 2–3 years of hands-on experience building, evaluating, and deploying ML models that have successfully shipped to production.
• Strong Python and SQL; independently explore large/complex datasets and feel comfortable being handed a data warehouse and finding your own way around it.
• Strong foundations in classical ML: tree-based models, regression, classification, clustering, feature engineering, and model evaluation.
• Strong rigor in experimentation and model evaluation: experiment design, appropriate metrics, statistical significance, and interpreting results.
• Comfort with cloud data platforms, particularly AWS and its data/ML ecosystem.
• Quantitative degree in Computer Science, Statistics, Mathematics, Engineering, Economics, a related field, or equivalent practical depth.
• Strong written and verbal English communication, with the ability to explain technical concepts to both technical and non-technical stakeholders.

Nice to Have

• FinTech AI/ML applications — risk, fraud, credit, customer analytics, and financial decisioning.
• AI/ML-driven campaign management — segmentation, targeting, and optimization.
• Causal inference and uplift modeling.
• Recommender systems — collaborative filtering, sequential recommendation, embeddings, and deep learning.
• LLM evaluation / Generative AI — hands-on experience with evaluation frameworks, prompt/model evaluation, RAG, agents, and the modern GenAI stack.