Data Scientist – Machine Learning & Analytics

About the role

Accenture Technology is looking for Data Scientists to develop analytical and machine‑learning models that generate measurable business value. The role works across early and intermediate stages of analytical maturity, partnering closely with Data Engineers and ML Engineers to operationalise solutions.

Key responsibilities

• Explore, clean, and prepare data for analysis and modelling.
• Design, build, and evaluate statistical and machine‑learning models.
• Run structured experiments, validate results, and document methodologies.
• Communicate insights to technical and business audiences.
• Collaborate with Data Engineers, ML Engineers, Product Owners, and Subject Matter Experts.
• Provide deployment artifacts and model documentation for MLOps hand‑over.
• Monitor model performance, detect data drift, and support retraining plans.

Required profile

• Strong proficiency in Python (NumPy, pandas, scikit‑learn) and basic knowledge of PyTorch or TensorFlow.
• Solid experience in exploratory data analysis, feature engineering, and statistical testing.
• Proven ability to build, evaluate, and tune supervised and unsupervised ML models.
• Proficiency in SQL for data querying and analysis.
• Experience with ML experimentation and tracking tools (MLflow, Weights & Biases, Databricks ML).
• Understanding of model evaluation, cross‑validation, and over‑fitting mitigation.
• Basic knowledge of cloud‑based ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
• Familiarity with data‑visualisation libraries and BI tools (Power BI, Tableau).

Required skills

• Python
• NumPy
• pandas
• scikit‑learn
• PyTorch
• TensorFlow
• SQL
• MLflow
• Weights & Biases
• Databricks ML
• Azure ML
• AWS SageMaker
• GCP Vertex AI
• Matplotlib
• Seaborn
• Plotly
• Power BI
• Tableau
• MLOps fundamentals (model versioning, registries, deployment lifecycle)