Senior Data Scientist

Position: Senior Data Scientist

At neXa, we’re not just building digital solutions — we’re helping businesses grow smarter. We work with forward-thinking clients across industries to design, build, and implement technology that makes a real difference. From intelligent automation to custom applications, our projects are as diverse as our team.

For one of our clients, we are looking for a Senior Data Scientist to join a high-energy team building advanced machine learning models for large-scale production environments. In this role, you will design, develop, and optimize deep learning models powering intelligent prediction systems, working closely with engineering and business stakeholders to transform strategic objectives into scalable AI solutions. You will play a key role in shaping modeling standards, driving technical excellence, and delivering measurable business impact.

Scroll down to see the full job description, including responsibilities and requirements:

Responsibilities:

• Design, develop, and optimize production-grade machine learning models for large-scale prediction and ranking systems

• Lead the end-to-end model development lifecycle, from problem definition and feature engineering to evaluation and production handoff

• Design advanced model architectures combining multiple data sources and signals for high-performance prediction systems

• Develop and improve deep learning models for business-critical applications

• Collaborate closely with Software Engineers, Product Managers, and Business stakeholders to translate strategic goals into technical solutions

• Ensure model quality, robustness, and readiness for production environments with strict performance and latency requirements

• Conduct experiments, evaluate model performance, and recommend improvements based on analytical results

• Build production-quality Python code following software engineering best practices Mentor Data Scientists and contribute to raising modeling standards across the team

• Contribute to the evolution of machine learning roadmaps, engineering practices, and AI capabilities

Requirements:

• Extensive hands-on experience designing, training, and improving Deep Learning models in production environments

• Strong practical expertise with neural networks and modern deep learning architectures

• Advanced experience with PyTorch and/or TensorFlow

• Strong programming skills in Python, including production-quality, testable, and maintainable code

• Advanced SQL skills, preferably in large-scale analytical environments

• Practical experience with Pandas and NumPy

• Experience developing end-to-end machine learning solutions, including feature engineering, model training, evaluation, and deployment

• Experience working with cloud-based machine learning platforms, preferably Google Cloud Platform, including Vertex AI and Vertex Pipelines

• Strong understanding of CI/CD practices and software engineering principles for machine learning Experience collaborating with engineering and business stakeholders to deliver production-ready AI solutions

• Experience mentoring other Data Scientists and promoting engineering and modeling best practices

• Degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or another STEM discipline, or equivalent professional experience

• Experience working with very large datasets in production environments

• Native-level Polish

• Good command of English (B2+ level or higher)

Nice to have:

• Experience developing machine learning models for advertising, marketing, or recommendation systems

• Experience building low-latency machine learning solutions for online inference

• Experience developing CTR, CVR, RoAS, or similar predictive models

• Practical knowledge of Gradient Boosted Trees and traditional machine learning techniques

• Experience in technical leadership or coordinating machine learning initiatives across teams

• Experience using AI-assisted coding tools to improve engineering productivity

• Experience working with Docker and containerized ML workloads