Senior Data Scientist

Location: Bulgaria; Croatia; Greece; Hungary; Latvia; Lithuania; Poland; Romania; Slovakia; Ukraine

Department: Technology

Why Cast AI?

Cast AI is the leading Application Performance Automation (APA) platform, enabling customers to cut cloud costs, improve performance, and boost productivity – automatically.

Built originally for Kubernetes, Cast AI goes beyond cost and observability by delivering real-time, autonomous optimization across any cloud environment. The platform continuously analyzes workloads, rightsizes resources, and rebalances clusters without manual intervention, ensuring applications run faster, more reliably, and more efficiently.

Headquartered in Miami, Florida, Cast AI has employees in more than 32 countries worldwide and supports some of the world’s most innovative teams running their applications on all major cloud, hybrid, and on-premises environments. Over 2,100 companies already rely on Cast - from BMW and Akamai to Hugging Face and NielsenIQ.

What’s next? Backed by our $108M Series C, we’re doubling down on making APA the new standard for DevOps and MLOps, and everything in between.

About the role

We are seeking a dynamic and innovative Senior Data Scientist to join our forward-thinking team. In this pivotal role, you will leverage data to drive operational excellence and spearhead the development of sophisticated machine learning models. Collaborate with top-tier Machine Learning and Data Engineers to bring these models into production and provide AI expertise across the organization, ensuring data-driven decision-making and innovative solutions.

Requirements:

• Experience: 5+ years of hands-on experience in data science and machine learning, with a proven portfolio of impactful projects.
• Modelling Expertise: Experience in training tabular and time-series models using classical machine learning and deep learning approaches.
• Technical Skills: Proficiency in building data pipelines, training models, and conducting experiments using industry-standard tools.
• Database Knowledge: Advanced SQL skills, including the ability to debug complex queries and a solid understanding of OLTP vs. OLAP systems (experience with OLAP systems is a plus).
• Problem-Solving: Exceptional analytical skills with a detail-oriented and innovative approach to challenges.
• Communication: Strong verbal and written communication skills to collaborate effectively within a team.

Bonus points

• Cloud & Containers: Experience with cloud platforms (AWS, GCP, Azure) and container tools like Kubernetes.
• ML Pipelines: Expertise in ML pipeline development, including data preprocessing, model training, deployment, and monitoring.
• MLOps Tools: Familiarity with MLOps tools like MLflow or Feast.
• OLAP Expertise: Hands-on experience with large-scale OLAP systems such as Clickhouse, BigQuery, or Snowflake.
• Advanced Modeling: Knowledge of advanced techniques for handling imbalanced datasets and other complex modeling challenges.

Responsibilities:

• Run Data Science Experiments: Design, execute, and evaluate experiments to refine models and improve outcomes.
• Develop Machine Learning Models: Create, validate, and maintain state-of-the-art algorithms, focusing on both tabular and time-series data
• Analyze Data: Perform deep-dive data analysis and deliver actionable insights to shape our business strategies.
• Collaborate: Work closely with cross-functional teams to align data science projects with company goals.
• Stay Current: Keep up with the latest advancements in data science, cloud technologies, and DevOps.
• Master Databases: Write, debug, and optimize complex SQL queries while effectively navigating OLAP and OLTP systems.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Enjoy a flexible, remote-first global environment.
• Equity options.
• Private health insurance.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.
  
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