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Manager- ML Ops
General Purpose
As a Manager Reporting & Automation in our recently set up team within the adidas Gurgaon Tech hub you will collaborate closely with the EU ecom Analytics team based in Amsterdam (the Netherlands) and other stakeholders located in other parts of the world (such as Herzogenaurach, Germany). You will be responsible for fully automating ML workflows and models and maintaining their stability. You will play a key role in enabling fact-based decisions through robust and trustworthy ML models. At the same time and on occasion, you will not shy away from performing basic data engineering tasks or mentoring data scientists into relevant ML Ops concepts.
Key Responsibilities
Consulting
Partner with the data scientists and data engineers in our team to ensure the stability and trustworthiness of ML models, across the entire ML Ops cycle.
Apply your expertise to drive the ML Ops discipline of the team to the next level.
Empower the business to use data science models with confidence through relevant upskilling, documentation, and self-service capabilities.
ML Ops
Create and maintain stable data & model pipelines (using Databricks MLflow).
Automate the data science workflow.
Optimize data science models with engineering techniques such as data storage and OOP improvements.
Determine how often data science models would need to be trained, tested, and deployed.
Work on versioning (like Bitbucket/Git/GitHub) and monitoring of training/predictions.
Create & maintain code repositories.
Produce data visualization such as charts and dashboards to communicate model findings, accuracy, and error rates to internal stakeholders clearly and effectively.
Work as part of Agile product teams (e.g. Scrum / Kanban) to deliver business value through data science models.
\xe2\x80\x9eIf required\xe2\x80\x9c Responsibilities
Data engineering
Perform data cleaning and transformation through repeatable workflows as needed for data science modelling.
Build data pipelines to be ingested into machine learning models\' pipelines, in collaboration with the data engineering & governance functions.
Key Relationships
EU ecom Analytics
Global Digital Analytics
EU eCom business teams
Global Data & Analytics
Global Digital Tech
Knowledge, Skills, and Abilities
Soft-Skills
Experience communicating & collaborating with cross-functional business stakeholders and cross-functional data colleagues (data scientists, analysts, data engineers, data quality experts, data governance, etc)
Able to explain ML Ops principles in simplistic words to less knowledgeable stakeholders
Clear written and oral communication skills.
Time management & prioritization skills
Critical thinking
Hard-Skills
Experience in deploying & maintaining ML models in production reliably and efficiently
Proficiency in Python (preferred) or R. Extensive knowledge of packages such as NumPy, Pandas, scikit-learn, etc. Knowledge of (py)Spark would be an added advantage.
Proficiency in SQL
Good data visualization skills and tool knowledge (i.e. Power BI, Tableau, MicroStrategy, matplotlib, plotly)
Familiarity with code version control & repository tools such as Git or Bitbucket
Comfortable working with enterprise-level platforms and technologies such as Databricks ML Flow (preferred) or AWS Sagemaker
Familiarity with the concepts of training, (unit) testing, CI/CD
Familiarity with agile way of working (scrum/Kanban)
Fluent in English both verbally and written.
Education & Professional Experience
A degree in software engineering, computer science, data science, mathematics, or a similar quantitative field
4+ years of experience in software engineering, ML Ops, data science, data engineering, or a similar function
AT ADIDAS WE HAVE A WINNING CULTURE. BUT TO WIN, PHYSICAL POWER IS NOT ENOUGH. JUST LIKE ATHLETES OUR EMPLOYEES NEED MENTAL STRENGTH IN THEIR GAME. WE FOSTER THE ATHLETE\'S MINDSET THROUGH A SET OF BEHAVIORS THAT WE WANT TO ENABLE AND DEVELOP IN OUR PEOPLE AND THAT ARE AT THE CORE OF OUR UNIQUE COMPANY CULTURE: THIS IS HOW WE WIN WHILE PLAYING FAIR.
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