As a global leader in digital transformation, we are expanding our Data Practice across Europe to address growing client demand for advanced Data Science and Machine Learning (ML) engineering services. We are seeking a talented and experienced
Data Science & ML Engineering Consultant
to join our dynamic team. This role emphasizes building scalable, production-ready ML solutions, optimizing model performance and driving technical innovation across diverse industries.
In this position, you will bridge the gap between data science and software engineering, delivering robust data-driven solutions that empower clients to solve real-world challenges and unlock measurable value.
Responsibilities
Collaborate with clients to define their data science and ML strategies, ensuring alignment with business objectives and technical feasibility Lead the design, development, deployment and maintenance of ML models, emphasizing MLOps best practices for scalability and reliability Design and implement data pipelines to process, transform and prepare data for ML workflows Monitor, evaluate and improve model performance, addressing issues like data drift, model drift and latency in production environments Build CI/CD pipelines for seamless integration of ML models into production systems Work with cross-functional teams, including data engineers, software developers and business stakeholders, to ensure the successful implementation of ML solutions Implement AI governance frameworks, ensuring compliance with ethical practices and industry regulations Stay at the forefront of industry trends, emerging ML technologies and innovative tools to continually enhance service offerings Translate complex ML concepts into actionable insights and technical roadmaps for stakeholders at various levels Contribute to client-facing activities, including presentations, workshops and responses to RFPs/RFIs Requirements
Bachelors or Masters degree in Data Science, Computer Science, Software Engineering or related fields. A Ph.D. is an advantage Extensive experience in data science, ML engineering or related roles Hands-on expertise in deploying ML models at scale in production environments Proficiency in Python and ML/engineering frameworks such as TensorFlow, PyTorch, Scikit-learn or similar tools Experience with MLOps tools, including MLFlow, Airflow, Kubernetes and Docker Strong knowledge of cloud platforms like Azure, AWS and GCP for deploying and managing ML models Familiarity with data engineering tools and practices (e.g., Spark, Databricks, SQL) Deep understanding of ML lifecycle management, including feature engineering, model selection and evaluation Expertise in building scalable and robust data pipelines to support production-grade ML workflows Awareness of AI/ML regulatory compliance and governance best practices Strong communication skills, capability to present technical concepts to technical and non-technical stakeholders We offer
EPAM Employee Stock Purchase Plan (ESPP) Protection benefits including life assurance, income protection and critical illness cover Private medical insurance and dental care Employee Assistance Program Competitive group pension plan Cyclescheme, Techscheme and season ticket loans Various perks such as free Wednesday lunch in-office, on-site massages and regular social events Learning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more If otherwise eligible, participation in the discretionary annual bonus program If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program *All benefits and perks are subject to certain eligibility requirements
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Data Science & ML Engineering Consultant
to join our dynamic team. This role emphasizes building scalable, production-ready ML solutions, optimizing model performance and driving technical innovation across diverse industries.
In this position, you will bridge the gap between data science and software engineering, delivering robust data-driven solutions that empower clients to solve real-world challenges and unlock measurable value.
Responsibilities
Collaborate with clients to define their data science and ML strategies, ensuring alignment with business objectives and technical feasibility Lead the design, development, deployment and maintenance of ML models, emphasizing MLOps best practices for scalability and reliability Design and implement data pipelines to process, transform and prepare data for ML workflows Monitor, evaluate and improve model performance, addressing issues like data drift, model drift and latency in production environments Build CI/CD pipelines for seamless integration of ML models into production systems Work with cross-functional teams, including data engineers, software developers and business stakeholders, to ensure the successful implementation of ML solutions Implement AI governance frameworks, ensuring compliance with ethical practices and industry regulations Stay at the forefront of industry trends, emerging ML technologies and innovative tools to continually enhance service offerings Translate complex ML concepts into actionable insights and technical roadmaps for stakeholders at various levels Contribute to client-facing activities, including presentations, workshops and responses to RFPs/RFIs Requirements
Bachelors or Masters degree in Data Science, Computer Science, Software Engineering or related fields. A Ph.D. is an advantage Extensive experience in data science, ML engineering or related roles Hands-on expertise in deploying ML models at scale in production environments Proficiency in Python and ML/engineering frameworks such as TensorFlow, PyTorch, Scikit-learn or similar tools Experience with MLOps tools, including MLFlow, Airflow, Kubernetes and Docker Strong knowledge of cloud platforms like Azure, AWS and GCP for deploying and managing ML models Familiarity with data engineering tools and practices (e.g., Spark, Databricks, SQL) Deep understanding of ML lifecycle management, including feature engineering, model selection and evaluation Expertise in building scalable and robust data pipelines to support production-grade ML workflows Awareness of AI/ML regulatory compliance and governance best practices Strong communication skills, capability to present technical concepts to technical and non-technical stakeholders We offer
EPAM Employee Stock Purchase Plan (ESPP) Protection benefits including life assurance, income protection and critical illness cover Private medical insurance and dental care Employee Assistance Program Competitive group pension plan Cyclescheme, Techscheme and season ticket loans Various perks such as free Wednesday lunch in-office, on-site massages and regular social events Learning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more If otherwise eligible, participation in the discretionary annual bonus program If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program *All benefits and perks are subject to certain eligibility requirements
#J-18808-Ljbffr
