Data Scientist

Who We Are

Provenir is the unified Decision Intelligence Platform that gives enterprises full control over end- to-end customer decisioning — to manage risk, drive growth, and transform business outcomes. By consolidating data, AI models, intelligence, agents and governance into a single decisioning environment, Provenir empowers business teams to configure and evolve strategy directly, while maintaining enterprise-grade reliability and regulatory compliance. Trusted by 120+ institutions in 60+ countries, Provenir processes over 4 billion decisions annually — turning architectural coherence into sustained risk performance and measurable value. Ready to solve meaningful challenges, work with enterprise customers, and help shape the future of AI-powered decisioning? Join us.

About Global Applied Intelligence

Global Applied Intelligence (GAI) is the team that turns Provenir’s platform capability into measurable client outcomes. We deploy data science, applied AI, and delivery expertise directly into client engagements, operating as trusted advisors and technical partners, not back-office implementers. GAI operates through regional business partnerships supported by global practices, meaning the right expertise reaches every engagement regardless of geography.

What You’ll Do

We’re seeking a hands-on Data Scientist to support Provenir’s growing business. In this role, you’ll help clients and internal teams understand, build, and apply data science solutions through our AI Decisioning platform.

You’ll work across a range of analytical and decisioning use cases, helping turn data, models, rules, and insights into practical business outcomes. These may include areas such as customer decisioning, risk analytics, fraud prevention, collections, customer management, operational efficiency, and broader business automation.

You’ll join Provenir’s global Data Science team and work closely with colleagues across Sales, Pre-Sales, GAI, Product, and Technology. This role blends hands-on analytical delivery with stakeholder communication and exposure to client-facing work. It is well suited to someone who enjoys solving real-world problems and wants to grow their ability to explain data science solutions in a commercial environment.

This is a remote role, based in India. Occasional travel may be required for regional team meetings and client engagement in Bengaluru.

Your 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.

Your Strengths and Skills

– 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.

– Although not essential, it would be great if you also have any of these strengths,

– 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.

Interview Process

The interview process would typically be structured as follows:…

Source: EU Remote Jobs (https://euremotejobs.com/job/data-scientist-6/)