Data Scientist

As a Machine Learning Engineer in this role, you will be responsible for working closely with customers, product teams, and engineering to achieve the following key responsibilities: - Onboard new clients and configure solutions to meet their data and business requirements. - Validate data quality and integrity to ensure accurate results. - Deploy and monitor machine learning models in production environments. - Execute existing ML pipelines for training new models and evaluating their quality. - Interpret model performance metrics and provide valuable insights to customers and internal teams. - Clearly communicate technical concepts to non-technical stakeholders. - Offer actionable feedback to product and R&D teams based on field experience. In addition to the above responsibilities, you will also collaborate with customers and internal teams to understand data, business context, and deployment requirements. You will perform data validation, enrichment, and transformation to prepare for modeling. Furthermore, you will execute pre-built ML pipelines, evaluate model performance metrics, monitor model behavior in production, troubleshoot data pipeline and model behavior issues, and provide clear explanations to customer stakeholders. The ideal candidate for this position is expected to have the following qualifications: - Bachelor's or master's degree in computer science, Data Science, or a related field with 5 to 10 years of experience. - Strong understanding of machine learning and data science fundamentals. - Proven experience in deploying and supporting ML models in production environments. - Experience in executing ML pipelines and interpreting model performance. - Excellent problem-solving and debugging skills. - Strong communication skills with the ability to simplify complex technical topics for non-technical audiences. - Experience working directly with customers or cross-functional teams. - Familiarity with monitoring tools and best practices for production ML systems. - Experience with cloud platforms, with preference for Azure or GCP. - Bonus: Experience in supply chain, retail, or similar domains. The technical environment you will be working in includes languages such as Python 3.*, SQL, frameworks/tools like TensorFlow, PyTorch, Pandas, NumPy, Jupyter, Flask, and Big Data & Cloud technologies like Snowflake, Apache Beam/Spark, Azure, GCP. Additionally, you will be involved in DevOps & Monitoring activities using tools like Docker, Kubernetes, Kafka, Pub/Sub, Jenkins, Git, TFX, and Dataflow. If you resonate with the values of the company, which are the driving force behind our success and that of our customers, we invite you to explore further: Core Values.,