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