Only 24h Left) Manager- ML Ops

Job Description

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

- 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.
- 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.

If required Responsibilities

- 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

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

COURAGE: Speak up when you see an opportunity; step up when you see a need.

OWNERSHIP: Pick up the ball. Be proactive, take responsibility and follow-through.

INNOVATION: Elevate to win. Be curious, test and learn new and better ways of doing things.

TEAMPLAY: Win together. Work collaboratively and cultivate a shared mindset.

INTEGRITY: Play by the rules. Hold yourself and others accountable to our company's standards.

RESPECT: Value all players. Display empathy, be inclusive and show dignity to all.

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