Senior Data Scientist - (Python, ML, Numpy , Professional Services)
25 Minutes ago • 5-10 Years
Data Analysis
Job Description
This Senior Data Scientist role involves working with customers, product, and engineering teams to onboard clients, configure solutions, validate data, deploy and monitor ML models, and execute ML pipelines. Responsibilities include interpreting model performance, communicating technical concepts, providing feedback for product enhancements, and troubleshooting issues. The role requires strong understanding of ML and data science fundamentals, with proven experience in production model deployment and customer collaboration.
Good To Have:
- Experience in supply chain, retail, or similar domains
Must Have:
- 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
- Bachelor’s or master’s in computer science, Data Science, or related field
- 5 to 10 years 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)
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.
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