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Project description We are seeking a highly skilled Senior MLOps Engineer / Data Scientist with a strong background in the Retail industry and Order-to-Cash (O2C) domains. The ideal candidate brings extensive development experience, including a deep foundation in programming and automation. In this role, you will bridge the gap between data science and production engineering. You will design, build, and maintain end-to-end Machine Learning pipelines. You will leverage Snowflake ML and Python to deploy scalable models. You will also use Azure DevOps for robust CI/CD automation. Additionally, you will translate complex data into actionable business insights using Power BI. 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, skilled 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. - Domain Experience: Deep understanding of the Retail industry and functional knowledge of the Order-to-Cash (O2C) process. - Education: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. - 9+ years of Domain Experience: Retail Industry, Demand forecasting, Inventory optimization, Customer analytics, Order-to-Cash (O2C) Billing, Cash application 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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.