Team Lead Data Scientist - Full Remote Portugal

Descrição do trabalho

This a Full Remote job, the offer is available from: Portugal

ABOUT THE OPPORTUNITY

Join a leading gaming and entertainment technology company as a Team Lead Data Scientist and drive machine learning initiatives that power data-driven decision making, automation, and personalized customer experiences across a global platform.

You'll be working for a technology-driven organization where data science and machine learning are core to the business strategy, enabling automated services while delivering tailored customer experiences at scale. The machine learning team builds diverse models ranging from binary classification tasks to sophisticated recommendation systems, transforming business needs into production applications across various business sectors utilizing different data types and handling broad project diversity.

As Team Lead, you'll combine strong technical expertise in machine learning with leadership responsibilities, guiding data scientists through the full model development lifecycle while ensuring delivery of high-quality production systems. Your team comprises data scientists, machine learning engineers, and data engineers, providing the complete skillset to deliver end-to-end projects from experimentation through production deployment and monitoring.

Critical Requirements: This is a lead-level position requiring background in Computer Science, Statistics, Math or related field with strong knowledge of machine learning algorithms and 2-8 years hands-on experience delivering ML models to production. MANDATORY expertise in Python machine learning ecosystem, Spark (PySpark), solid OOP software background, team leadership experience, and strong skills in teamwork, communication, and analytical thinking. English fluency (B2+) essential for team leadership and stakeholder communication.

PROJECT & CONTEXT

You'll be leading machine learning initiatives for a global gaming and entertainment platform where data-driven decisions enable service automation and personalized customer experiences at massive scale. The company's aim is leveraging machine learning across diverse business sectors to optimize operations, enhance user engagement, and deliver value through intelligent automation and recommendation systems that serve millions of active users.

Your team leadership responsibilities center on guiding data scientists through the complete model development process, from translating business requirements into machine learning problems through production deployment and monitoring. You'll mentor team members on best practices, facilitate knowledge sharing, coordinate with machine learning engineers and data engineers for production implementation, and ensure the team delivers high-quality models that solve real business challenges effectively.

Translating business requirements into machine learning problems is a core competency - you'll work with stakeholders to understand business objectives, identify appropriate ML approaches, define success metrics, and design experiments that validate model effectiveness. Your ability to bridge business needs and technical solutions ensures the team focuses on high-impact projects that deliver measurable business value.

Exploratory Data Analysis (EDA) and feature engineering form the foundation of model development - you'll guide the team in understanding data distributions, identifying patterns and relationships, handling missing data and outliers, and engineering features that capture business logic and improve model performance. Your analytical thinking and experimental design skills ensure thorough data understanding before model training begins.

Comparative experiments for model training require rigorous methodology - you'll implement best practices for model selection comparing different algorithms and architectures, parameter tuning using systematic approaches like grid search or Bayesian optimization, cross-validation strategies that ensure robust performance estimates, and evaluation metrics appropriate for business objectives. Understanding ML algorithm theory enables informed decisions about model architecture and training approaches.

The Python machine learning ecosystem is your primary toolset - you'll leverage libraries including scikit-learn for classical ML algorithms, pandas for data manipulation, NumPy for numerical computing, and visualization tools for analysis and communication. Your deep knowledge of Python ML tools enables efficient model development and experimentation.

Spark (PySpark) expertise is essential for processing large-scale data - you'll design and implement distributed data processing pipelines, train models on massive datasets using Spark MLlib or integrating with other ML frameworks, optimize Spark jobs for performance and resource efficiency, and handle the unique challenges of distribut