Data Scientist (Professional Services)

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.