Data Scientist at Moniepoint

Job Overview

• At Moniepoint, data is at the core of everything we do. We are a customer-centric company, and your work will enable our teams to make informed, data-driven decisions that directly impact the success of our business.
• We’re looking for an experienced Senior Data Scientist who’s excited to solve meaningful problems, collaborate across teams, and turn complex data into clear, actionable insights.
• You’ll work at the intersection of data, product, and engineering to uncover insights, guide strategic decisions, and develop models that enhance user experience and business outcomes.

Key Responsibilities

• Lead high-impact projects: Design and deliver end-to-end data science solutions that support product innovation and business strategy.
• Uncover insights: Analyze large, complex datasets to identify trends, surface opportunities, and influence key decisions.
• Build models: Develop and deploy predictive and prescriptive models using machine learning and statistical techniques.
• Enable experimentation: Design A/B tests and causal inference studies to help teams learn quickly and make informed choices.
• Collaborate cross-functionally: Work closely with product managers, engineers, and business leaders to understand goals and deliver data-driven solutions.
• Promote data fluency: Build dashboards, tools, and frameworks to enable self-service analytics and scale your impact across teams.

Qualifications

• BSc / MSc / PhD in a quantitative field such as Statistics, Computer Science, Mathematics, Economics, or similar.
• 5+ years of experience as a Data Scientist, ideally in fast-paced or high-growth environments
• Proficiency in SQL and experience working with large-scale data systems (e.g., Redshift, BigQuery, Snowflake).
• Strong analytical and statistical skills; fluency in Python or R.
• Experience with machine learning libraries (e.g., scikit-learn, XGBoost) and data visualization tools (e.g., Tableau, Looker, Plotly).
• Solid understanding of experimental design, hypothesis testing, and causal inference.
• Ability to distill complex data problems into clear, actionable insights.

Experience with the following would be a plus:

• Experience with deep learning, NLP, or time-series forecasting.
• Knowledge of tools for building production data pipelines (e.g., Airflow, dbt).
• Familiarity with business domains like fintech, e-commerce, healthcare, etc.

Method of Application