Data Science & Modelling Associate

Job No.: BRQ-EMEA-HYD-DSM-019
Location: Hyderabad, India
Region: Bruqe EMEA
Team: Data Science & Modelling
Level: Associate
Employment Type: Full Time
Reporting To: Engagement Lead, EMEA

The Role

Bruqe is seeking a Data Science & Modelling Associate to develop analytical models that support strategic decision-making under uncertainty.

You will work at the intersection of data science, systems intelligence, and advisory. The role involves transforming complex and disparate datasets into robust analyses, causal hypotheses, scenario models, and decision tools that help clients understand the drivers of an outcome—not simply the correlations surrounding it.

What You Will Do

• Build, test, and refine quantitative models supporting strategic, market, operational, and investment-related decisions.

• Prepare and integrate data from structured and unstructured sources, maintaining high standards of quality, traceability, and reproducibility.

• Apply statistical, econometric, predictive, and simulation-based methods to examine key drivers, dependencies, and potential outcomes.

• Support causal analysis by developing and testing hypotheses around mechanisms, levers, and second-order effects.

• Collaborate with advisory and research colleagues to ensure models reflect relevant market, technology, policy, and operational context.

• Translate technical outputs into clear visualisations, decision frameworks, and executive-ready insights.

• Contribute to Bruqe’s modelling standards, reusable analytical components, and proprietary decision-intelligence platform.

What You Will Bring

• An undergraduate or postgraduate degree in data science, statistics, mathematics, economics, engineering, computer science, operations research, or a related quantitative discipline.

• Experience in data science, analytics, consulting, research, financial modelling, or a comparable problem-solving environment.

• Proficiency in Python, R, SQL, or comparable analytical languages; experience with statistical and data-processing libraries is expected.

• A strong grounding in statistical analysis, data modelling, and hypothesis-driven problem-solving.

• Familiarity with forecasting, machine learning, econometrics, optimisation, simulation, or causal inference is advantageous.

• The ability to communicate technical findings clearly to non-technical colleagues and senior decision-makers.

• Intellectual rigour, attention to detail, and an interest in applying analytical methods to real strategic questions.

What Success Looks Like

You will build models that are technically robust, contextually grounded, and useful in real decisions. Your work will help engagement teams identify the variables that matter most, test strategic assumptions, and give clients a clearer view of the choices available to them.