Senior MLOps Engineer / Data Scientist
A senior role leading end-to-end design, deployment, and monitoring of ML pipelines for supply chain and retail analytics (demand forecasting, inventory, Order-to-Cash) using Python, Snowflake ML/Databricks/Azure ML, Azure DevOps CI/CD, and Power BI. Also drives GenAI/LLM/RAG initiatives for intelligent automation.
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data science
- Posted
Skills
Must have
- 10+ years of experience in Data Science, Machine Learning, Advanced Analytics, and AI solution delivery
- Minimum 5+ years of hands-on professional Python development experience writing production-grade code
- Expert-level Python with strong experience in Machine Learning libraries and statistical modeling
- Proven experience in Supply Chain Analytics: Demand Planning, Inventory Management, Logistics, Warehousing, Sales, Distribution
- Strong expertise in Forecasting and Optimization techniques (demand forecasting, inventory optimization, network optimization, capacity planning)
- Hands-on experience with Snowflake ML, Databricks ML, or Azure Machine Learning platforms
- Hands-on experience with Snowflake ML tools (Snowpark, Cortex AI, or Feature Store)
- Advanced knowledge of SQL, Data Modeling, Feature Engineering, and Enterprise Data Platforms
- Strong experience in MLOps and DevOps including Azure DevOps, Git, CI/CD pipelines, model deployment, monitoring, release management
- Proven experience with Azure DevOps, Git, and automated CI/CD workflows
- Proficiency in Power BI for executive dashboards, reporting, and data visualization, including DAX and data modeling
- Deep understanding of the Retail industry and functional knowledge of the Order-to-Cash (O2C) process
- Ability to engage with senior stakeholders, translate business requirements into analytical solutions, and present insights to leadership
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field
Nice to have
- Azure Machine Learning, Docker, Kubernetes, MLflow
- Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch
- Snowflake SQL, ETL/ELT, Data Warehousing, Azure Data Factory, Power Query
- Time Series Forecasting, Demand Forecasting, Inventory Management, Customer Analytics, Credit Risk Analytics
- REST APIs, PyTest, Agile/Scrum, Statistics, Feature Engineering, Experiment Tracking
Full description
Project description
We are seeking an experienced Senior Data Scientist to lead the design, development, and deployment of advanced AI, Machine Learning, and Supply Chain Analytics solutions. The role will focus on solving complex business challenges across Supply Chain, Demand Planning, Inventory Optimization, Logistics, Warehousing, Sales, and Distribution through predictive analytics, optimization techniques, and AI-driven decision support systems.
The ideal candidate will have strong expertise in Python, Machine Learning, Forecasting, Optimization Models, Snowflake ML/Databricks ML, Azure ML, and MLOps practices, along with proven experience delivering enterprise-scale analytics solutions. The candidate will collaborate with business stakeholders, architects, data engineers, and leadership teams to implement scalable analytical platforms that improve operational efficiency, forecasting accuracy, inventory management, and business performance.
Additionally, the role will drive innovation through Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and Agentic AI solutions, enabling intelligent automation and next-generation business insights.
Responsibilities
- Key Responsibilities
- End-to-End MLOps: Design, deploy, and monitor scalable ML pipelines from data ingestion to model deployment and retraining.
- Snowflake ML Development: Utilize Snowpark, Snowflake Cortex AI, and Model Registry to build and manage in-data-warehouse machine learning solutions.
- Pipeline Automation: Build and maintain CI/CD pipelines using Azure DevOps for seamless, automated model deployment and testing.
- Domain Analytics: Apply ML models to optimize the Order-to-Cash (O2C) lifecycle, improving cash application, billing efficiency, and credit risk assessments.
- Retail Solutions: Deliver data-driven solutions for retail use cases, including demand forecasting, inventory management, and customer analytics.
- Business Intelligence: Create interactive Power BI dashboards and data models to translate complex ML outputs into clear executive insights.
Required Skills & Qualifications (Mandatory)
- Python Expertise: Minimum of 5+ years of hands-on, professional Python development experience writing clean, production-grade code.
- Snowflake Ecosystem: Hands-on experience with Snowflake ML tools (Snowpark, Cortex AI, or Feature Store).
- DevOps Tools: Proven experience with Azure DevOps, Git, and automated CI/CD workflows.
- BI Tools: Strong knowledge or experience with Power BI, including DAX and data modeling techniques.