Associate Data Scientist - Builders Program - (Emirati National)

Role Overview

Associate Data Scientist - Builders Program for Emirati Nationals at Tamara, a Saudi FinTech unicorn. This is an early-career role designed for fresh graduates or those with up to two years of experience, offering immediate high-impact responsibility and accelerated growth within Tamara's flagship Builders Program.

Company Overview

Tamara is Saudi Arabia's first FinTech unicorn, on a mission to help people own their dreams by building the most customer-centric financial super app in the world. The organization is composed of innovators, problem-solvers, and learners who thrive on curiosity and collaboration. The Builders Program is a career accelerator designed for ambitious graduates ready to step into real responsibility from day one, placing talent directly into high-impact roles across Product, Engineering, Design, and beyond with January and June cohorts available.

Role Purpose

This role blends product thinking, applied statistics, and production-minded analytics to solve ambiguous problems and create measurement frameworks, lightweight models, and AI-assisted decision workflows that improve customer outcomes and business performance across the fintech platform.

Key Responsibilities

Problem Definition and Measurement

• Translate product or business questions into testable hypotheses and clear success metrics
• Build and maintain metric definitions and analysis templates so decisions are consistent and repeatable
• Turn ambiguous problems into measurable questions

Experimentation and Causal Thinking

• Design, analyze, and interpret A/B tests and quasi-experiments when randomization is not possible
• Partner with product and engineering to ensure correct tracking, guardrails, and experiment quality

Modeling for Decisions

• Build baseline predictive and segmentation models (for example, churn propensity, risk signals, customer clustering) with clear evaluation and limitations
• Focus on models that are actionable: identify who will use them, when, and what decision they change

Applied AI for Analytics and Decisioning

• Prototype small, safe LLM use cases that improve how teams explore data (for example, natural-language Q&A over a curated dataset, summarizing experiment results, or generating investigation checklists)
• Help define evaluation and validation approaches for AI-assisted outputs (sanity checks, golden queries, offline test sets, and human review loops)
• Partner with platform and security stakeholders to ensure AI workflows are permissioned correctly and avoid leaking sensitive data

Analytics Infrastructure and Reliability

• Create reusable, well-documented analysis assets: datasets, feature tables, notebooks, dashboards, and simple services or jobs
• Collaborate with data engineering to productionize reliable pipelines where needed
• Validate data inputs, monitor key metrics, and investigate anomalies
• Document assumptions and build sanity checks so analyses and models are trustworthy

Responsible AI Use

• Use AI to accelerate coding, documentation, and exploration
• Validate AI outputs, protect sensitive data, and follow safe data handling practices

Stakeholder Partnership

• Partner closely with engineering, product, and risk to improve customer outcomes and business performance

Qualifications & Experience

• Fresh graduate or relevant experience (internships, capstone projects, or part-time roles count)
• Up to two years of early-career experience

Skills & Competencies

Must Have

• Strong SQL fundamentals (joins, aggregations, window functions)
• One programming language for data (preferably Python) with basic skills in data manipulation (tables/dataframes), statistics fundamentals (distributions, sampling intuition, confidence basics), and basic modeling (regression/classification basics and evaluation intuition)
• Strong analytical thinking with ability to define a problem, validate data, and explain results clearly
• Strong attention to detail and commitment to accurate, reliable outputs
• Ability to work effectively in a team-oriented environment

Nice to Have

• Exposure to experimentation platforms or frameworks (for example, Statsig, Optimizely, internal experimentation tooling)
• Familiarity with modern analytics stacks (dbt, BigQuery, Snowflake, Looker, PowerBI, Tableau) through coursework or projects
• Exposure to ML tooling (scikit-learn, notebooks, basic MLOps concepts) and version control (Git)
• Familiarity with LLM concepts (prompting basics, retrieval/RAG intuition, and evaluation approaches) through coursework or side projects
• Understanding of product analytics concepts (funnels, cohorts, retention) and causal pitfalls (selection bias, confounding)
• Experience creating AI-ready data assets (clean semantic layers, metric definitions, data contracts, documentation, and sanity-check checklists)
• Knowledge of responsible data handling (PII basics, access controls, safe sharing)

Additional Information

What Success Looks Like

• Independently deliver an end-to-end analysis with clear assumptions, validation steps, and a concrete recommendation
• Ship at least one decision tool (experiment, metric framework, or lightweight model) that changes a product or business decision
• Enable stakeholders to run repeatable analyses with less back-and-forth, reducing ambiguity in key metrics
• Spot when results look off, debug quickly, and explain the root cause