Data Scientist

The Data Scientist is responsible for developing analytical and machine-learning solutions that support decision-making across customer, commercial, supply chain, store operations, loss and corporate domains.

The role works with business stakeholders, product teams, data engineers and AI/ML engineers to translate business problems into measurable analytical outcomes. It covers data exploration, statistical analysis, feature development, predictive modelling, experimentation, model evaluation and ongoing performance monitoring.

The role is expected to deliver practical, explainable and production-ready analytical solutions rather than standalone research or proof-of-concept models.

Key Responsibilities

Data Science and Modelling

Translate business problems into clearly defined analytical hypotheses and modelling approaches.

Explore and analyse large structured and semi-structured datasets.

Develop predictive, classification, forecasting, optimisation and segmentation models.

Undertake feature selection, feature engineering and model experimentation.

Evaluate models using appropriate statistical and commercial performance measures.

Compare model approaches and document assumptions, limitations and trade-offs.

Develop interpretable outputs that support business decision-making.

Contribute to recommendation, pricing, promotion, demand, customer and operational analytics use cases.

Experimentation and Measurement

Design and analyse experiments, including A/B tests and controlled trials.

Establish appropriate control groups, success measures and evaluation criteria.

Assess model and initiative performance against agreed business outcomes.

Support causal analysis, incrementality measurement and scenario modelling.

Communicate statistical confidence, uncertainty and limitations clearly.

Productionisation and Model Lifecycle

Work with AI/ML engineers and data engineers to productionise models.

Develop reusable and maintainable Python and SQL code.

Support model deployment, validation, monitoring and retraining processes.

Monitor model accuracy, drift, bias and business performance.

Maintain model documentation, feature definitions and evaluation evidence.

Contribute to model governance, approval and risk-management activities.

Investigate model-performance issues and recommend corrective actions.

Stakeholder Collaboration

Work with product owners and business stakeholders to define analytical requirements.

Explain complex modelling outcomes in clear business language.

Present insights, recommendations and commercial implications.

Work within cross-functional product and engineering teams.

Support prioritisation of analytical opportunities based on value, feasibility and data readiness.

Required Skills and Experience

Experience delivering data-science or advanced-analytics solutions in an enterprise environment.

Experience with Snowflake, Databricks or comparable cloud data platforms.

Experience with Azure-based data and machine-learning services.

Strong Python skills, including experience with pandas, NumPy, scikit-learn or equivalent libraries.

Strong SQL capability and experience working with large analytical datasets.

Practical knowledge of statistical modelling, machine learning and experimental design.

Experience developing models such as:

Classification and regression

Time-series forecasting

Segmentation and clustering

Recommendation or propensity models

Optimisation or scenario models

Experience with data preparation, feature engineering and model evaluation.

Ability to translate analytical outputs into business recommendations.

Experience using Git and collaborative software-development practices.

Strong communication, documentation and stakeholder-management skills.

Tertiary qualification in data science, statistics, mathematics, computer science, engineering, econometrics or a related discipline

Desirable Skills

Exposure to MLflow, feature stores, model registries or MLOps practices.

Experience with Power BI, MicroStrategy or comparable visualisation platforms.

Knowledge of retail, customer, commercial, pricing, promotion, supply chain or store operations.

Experience with causal inference, optimisation, operations research or econometrics.

Exposure to generative AI, natural-language processing or computer vision.

Experience working in Agile product teams.

Key Deliverables and Success Measures

Accurate, explainable and commercially relevant analytical models.

Measurable improvement in agreed business or operational outcomes.

Models successfully transitioned into production and operational use.

Robust experimentation and model-evaluation evidence.

Effective monitoring of model accuracy, drift and business performance.

Reusable code, documented assumptions and traceable analytical outputs.

Positive engagement with product, engineering and business stakeholders.

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Benefits of working at Accenture:

18 weeks paid parental leave

Long & short-term career break opportunities

Structured career development program

Local and international career opportunities.

Certified as a Family Inclusive Workplace™

Flexible Work Arrangements - centered around Accenture’s Truly Human ethos and our commitment to supporting the health and wellbeing of our people.

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