Other key responsibilities will include:
- Build real-world scalable machine learning pipelines and deploy them to production
- Operate at the intersection of data science and software engineering to create analytics solutions
- Produce high-quality code that allows us to put solutions into production
- Automate ML workflows and deploy models into production environments
- Lead the thinking on choosing and using the right analytical libraries, programming languages, and frameworks
- Build analytics libraries and tooling based on project experience and the latest research, refactor code into reusable libraries, APIs, and tools
- Develop and execute comprehensive QA tests for data science pipelines
- Automate testing processes to improve efficiency and consistency
- Play an active role in leading team meetings and workshops to inform product development and process evolution
What you’ll learn:
- Successful projections on real-world problems across Retail use cases are achieved by referencing past deliveries of end-to-end pipelines.
- Build products alongside the Core engineering team and evolve the engineering process to scale with data, handling complex problems and advanced client situations.
- Be focused on the wrangling, clean-up, and transformation of data by working alongside the Data Science team which focuses on modeling the data.
- Using new technologies and problem-solving skills in a multicultural and creative environment.
Your qualifications and skills
- Bachelor’s degree in computer science, engineering, mathematics, or equivalent experience
- 3+ years of relevant experience with strong foundations of statistics and machine learning techniques
- Strong understanding of machine learning algorithms, model training, validation, and deployment; proficiency in popular ML frameworks such as TensorFlow, PyTorch, and scikit-learn
- Proven experience writing production-grade code (Python / PySpark) for machine learning in a professional setting and building scalable machine learning pipelines
- Proficiency in software development practices, version control (e.g., Git), CI/CD pipelines, containerization (Docker), and orchestration tools (Kubernetes)
- Familiarity with cloud platforms (AWS, GCP, Azure, Databricks) and infrastructure as code (Terraform, CloudFormation)
- Familiarity or hands-on experience with data preprocessing, feature engineering, and building scalable data pipelines using tools like Spark, Kafka, and ETL processes.
- Knowledge of database management (SQL, NoSQL) and data warehousing solutions
- Familiarity or hands-on experience with data visualization tools (PowerBI, Tableau, etc.)