Data Scientist

Provenir

3d•Remote

About The Position

Provenir is seeking a hands-on Data Scientist to support its growing business. This role involves helping clients and internal teams understand, build, and apply data science solutions through Provenir's AI Decisioning platform. The Data Scientist will work across various analytical and decisioning use cases, translating data, models, rules, and insights into practical business outcomes in areas such as customer decisioning, risk analytics, fraud prevention, collections, customer management, operational efficiency, and business automation. The position is part of Provenir’s global Data Science team, collaborating with Sales, Pre-Sales, GAI, Product, and Technology. The role combines hands-on analytical delivery with stakeholder communication and client-facing exposure, suitable for individuals who enjoy solving real-world problems and developing their ability to explain data science solutions in a commercial context. This is a remote role based in India, with occasional travel for regional team meetings and client engagements in Bengaluru.

Requirements

Bachelor’s degree in a STEM field plus a minimum of 3 years of experience in data science, analytics, decision science, applied machine learning, or a related field; or a master’s degree or equivalent experience in a related STEM field.
Experience building, validating, or interpreting machine learning models in Python.
Strong data manipulation skills, including merging, cleansing, sampling, profiling, and preparing data for analysis.
Practical, hands-on experience using AI tools (e.g. Copilot, Codex, Claude Code, OpenCode, etc) to support coding, analysis, or delivery work.
Ability to balance model performance, explainability, complexity, and business usability.
Good understanding of common model evaluation concepts such as AUC, precision, recall, lift, stability, and model monitoring.
Ability to translate analytical outputs into clear insights and recommendations.
Confident, comfortable communicating directly with clients as well as internal stakeholders, as part of project delivery and discovery.
Curious, proactive, and willing to learn new business domains, analytical methods, and platform capabilities.
Organized and delivery-focused, with the ability to manage multiple priorities in a fast-moving environment.

Nice To Haves

Experience in financial services, fintech, banking, lending, payments, insurance, telecommunications, or another data-rich industry.
Exposure to client-facing work, workshops, product demonstrations, or cross-functional business discussions.
Experience with MLOps, model deployment, APIs, MLflow, CI/CD, or production model governance.
Familiarity with credit risk, fraud, collections, or customer management use cases.
Experience preparing presentations, technical documentation, or business summaries for data science initiatives.

Responsibilities

Support client engagements across a range of data science, analytics, and decisioning use cases.
Work with internal teams and, where appropriate, clients to understand business problems, data availability, analytical requirements, and expected outcomes.
Build, evaluate, and explain analytical solutions, including predictive models, scorecards, decision rules, segmentation analysis, simulations, and business insights.
Use Python and common data science libraries to clean, explore, transform, and analyze data.
Help prepare analytical outputs, client-ready insights, model performance summaries, and business recommendations.
Contribute data science input to discovery, workshops, demonstrations, and delivery activities, with support from more experienced team members.
Help explain how machine learning, rules, explainability, monitoring, and decisioning can be applied within the Provenir platform.
Communicate technical concepts clearly to both technical and non-technical audiences.
Document analysis, assumptions, code, and recommendations in a clear and reproducible way.
Follow and contribute to team best practices for code quality, documentation, version control, testing, and reusable delivery assets.
Collaborate with global Data Science colleagues to share learnings, improve internal approaches, and support consistent delivery across regions.