Data Scientist - ML Engineer

July 27

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Data Science • Business Intelligence • Data Warehousing • Machine Learning • Artificial Intelligence

51 - 200

Description

• Develop, implement, and validate Machine Learning solutions tailored to specific project requirements. • Understand clients' needs and translate business problems into data science problems. • Design scalable AI solution architectures • Develop data pipelines and workflows, ensuring the highest standards of data quality and security. • Collaborate with Data Engineering and Software Engineering teams to build and deploy AI applications. • Present findings and recommendations clearly and concisely to stakeholders. • Effectively plan tasks within an Agile/Scrum framework.

Requirements

• 4+ years of commercial experience in designing and implementing scalable AI solutions (Machine Learning, Predictive Modeling, Optimization, NLP, Computer Vision, GenAI) • Proficiency in developing ML algorithms from scratch to production deployment • Strong programming skills in Python: writing clean code, OOP design, extensive knowledge of ML libraries (Scikit-Learn, PyTorch / Tensorflow) • Proven expertise in deploying solutions in cloud environments (AWS or Azure) • Knowledge in applying LLMs (semantic search, prompt engineering, multimodal embeddings, RAG) • High proficiency in English (C1 level) • Excellent communication skills and consulting experience with direct interaction with clients. • Working experience in SQL and NoSQL databases (MongoDB, Snowflake, Databricks) • Good knowledge of CI / CD principles (GitHub and GitHub Actions) • Familiarity with MLOps practices, Kubernetes, and Docker • Master’s or Ph.D. in Computer Science, Data Science, Mathematics, Physics, or a related field • Experience with Big Data technologies like Spark, Hadoop, and Kafka is a plus.

Benefits

• Flexible work arrangements, allowing remote work or from modern offices and coworking spaces. • 20 fully paid days off available for B2B contractors and individuals under contracts of mandate. • Medical and sports packages, eye care, and well-being support services, including psychotherapy and coaching. • Full work equipment for optimal productivity, including a laptop and other necessary devices. • Participate in team-building events and utilize the integration budget. • Celebrate work anniversaries, birthdays, and milestones.