BlueYonder - Senior Data Scientist

Scope:

You will work closely with customers, product teams, and engineering to:

- Onboard new clients and configure solutions to their data and business needs.

- Validate data quality and integrity.

- Deploy and monitor machine learning models in production.

- Execute existing ML pipelines to train new models and assess their quality.

- Interpret model performance and provide insights to both customers and internal teams.

- Communicate technical concepts clearly to non-technical stakeholders.

- Provide actionable feedback to product and R&D teams based on field experience.

Our Technical Environment:

- Languages: Python 3.-, SQL

- Frameworks/Tools: TensorFlow, PyTorch, Pandas, NumPy, Jupyter, Flask

- Big Data & Cloud: Snowflake, Apache Beam/Spark, Azure, GCP

- DevOps & Monitoring: Docker, Kubernetes, Kafka, Pub/Sub, Jenkins, Git, TFX, Dataflow

What you'll do:

- Collaborate with customers and internal teams to understand data, business context, and deployment requirements.

- Perform data validation, enrichment, and transformation to ensure readiness for modelling.

- Execute pre-built ML pipelines to train and retrain models using customer data.

- Evaluate and interpret model performance metrics to ensure quality and stability.

- Monitor model behaviour and data drift in production environments.

- Troubleshoot issues related to data pipelines, model behaviour, and system integration.

- Clearly explain model behaviour, configuration, and performance to customer stakeholders.

- Gather insights from customer engagements and provide structured feedback to product and engineering teams to drive product enhancements.

- Document processes and contribute to playbooks for scalable onboarding.

- Train and mentor junior PS team members.

What We're Looking For:

- Bachelor's or master's in computer science, Data Science, or related field with 5 to 10yrs of experience

- Strong understanding of machine learning and data science fundamentals.

- Proven experience deploying and supporting ML models in production.

- Experience executing ML pipelines and interpreting model performance.

- Excellent problem-solving and debugging skills.

- Strong communication skills with the ability to explain complex technical topics to 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 (Azure or GCP preferred).

- Bonus: Experience in supply chain, retail, or similar domains.