Senior ML Engineer (вакансія неактивна)

Project description:

We are seeking a passionate and innovative Machine Learning Engineer to join our growing team. In this role, you will leverage your deep understanding of machine learning principles, data preprocessing techniques, ML algorithms and software engineering best practices to transform business needs into actionable ML solutions. You will work closely with cross-functional teams, including ML researches, data scientists, data engineers, product managers, and other stakeholders to build, optimize, and deploy ML models that drive key insights and decisions.

Required Qualifications:

• Experience: Minimum 3-5 years of experience in a machine learning engineering role or a similar capacity with a proven track record of developing and deploying production ML models.Technical Proficiency:Languages: Strong proficiency in Python, with hands-on experience in SQL and Snowflake.
• ML Frameworks: Expertise in ML frameworks such as TensorFlow, PyTorch, or similar. Data Handling: Experience working with various data sources and formats, including large-scale structured and unstructured datasets.
• Algorithms: Solid understanding of supervised and unsupervised machine learning algorithms, including regression, classification, clustering, and deep learning techniques.
• Visualization: Proficiency in visualization tools such as Seaborn, Matplotlib, Tableau, or similar, with the ability to effectively communicate data insights to both technical and non-technical audiences.
• Problem-Solving: Ability to take abstract business requirements and transform them into concrete, actionable data and ML problem statements.
• Communication and Collaboration: Strong interpersonal skills with a demonstrated ability to collaborate effectively across teams, including data science, engineering, and business units.Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.

Nice-to-have:

• Familiarity with MLOps principles and tools for continuous integration/continuous deployment of ML models
• Big Data and ML Familiarity: Experience working with big data technologies (e.g., Spark, Hadoop) and applying ML techniques to large-scale datasets.

Key Responsibilities:

• Data Exploration and Preprocessing: Conduct thorough data exploration and apply preprocessing techniques to prepare large and complex datasets from multiple sources for analysis and modeling.
• Data Management and Integration: Utilize SQL, Snowflake, and other database tools to efficiently query and integrate data from multiple sources and formats, ensuring data quality and reliability.
• Model Development and Deployment Support: Support design, build, and deployment of machine learning models that solve real-world problems, focusing on high scalability and performance across a range of applications.
• Performance Optimization: Continuously evaluate model performance, optimizing models to improve accuracy and reduce latency while balancing cost and scalability.
• Collaboration with Stakeholders: Work closely with business and technical teams to translate general business requests into concrete data and ML problem statements, ensuring alignment and transparency in ML initiatives.Documentation and Knowledge
• Sharing: Document processes, models, and key insights, promoting best practices and supporting knowledge-sharing initiatives within the team.