Data Scientist

Accenture Technology powers our clients to achieve high performance. We combine business and industry insights with innovative technology to drive growth for your business. We extend our technology and business capabilities through a powerful alliance ecosystem of market leaders and innovators to provide our clients the best specialized skills and tailored solutions.

Job Summary

We are seeking qualified Data Scientists to join our team. This role is responsible for developing analytical and machine learning models that deliver measurable business value.

The Data Scientist plays a key role in early and intermediate stages of analytical maturity, collaborating closely with Data Engineers and ML Engineers to ensure successful operationalization. We are open to candidates at different levels of seniority.

Qualifications

Strong proficiency in Python, including NumPy, pandas, and scikit-learn, with basic knowledge of PyTorch or TensorFlow

Solid experience in exploratory data analysis (EDA) and feature engineering

Strong foundation in statistics and probability, including hypothesis testing, inference, and distributions

Experience building, evaluating, and tuning supervised and unsupervised machine learning models

Proficiency in SQL for data analysis and querying

Experience with ML experimentation and tracking tools such as MLflow, Weights & Biases, or Databricks ML

Understanding of model evaluation and validation strategies, including cross-validation, metrics, and overfitting

Knowledge of cloud-based ML platforms such as Azure ML, AWS SageMaker, Fabric, or GCP Vertex AI

Experience with data visualization libraries (Matplotlib, Seaborn, Plotly) and BI tools (Power BI or Tableau)

Understanding of MLOps fundamentals, including model versioning, registries, and deployment lifecycle

Responsibilities

Explore, clean, and prepare data for analysis and modeling

Design, build, and evaluate statistical and machine learning models

Run structured experiments and validate results using sound scientific methods

Document methodologies, assumptions, metrics, and key decisions

Communicate insights and results clearly to both technical and business audiences

Collaborate closely with Data Scientists, Data Engineers, ML Engineers, Product Owners, and Subject Matter Experts

Support MLOps handover by providing deployment artifacts and model documentation

Monitor model performance and data drift, contributing to retraining and improvement plans

Ensure alignment between analytical solutions and business objectives

Additional Information

Location: Lisbon

Working Model: Hybrid