DATA ANALYST - MACHINE LEARNING ENGINEER
PURPOSE & OVERALL RELEVANCE FOR THE ORGANIZATION
A Machine Learning Engineer is a specialist who applies its expertise in end-to-end Model Development Lifecycle (MDLC), artificial intelligence and engineering - software, DevOps, data, cloud, and platform; to improve and productionize state of the art machine learning models. They can pivot between data centric (data engineering) or model centric (data science) approaches to enhance predictive model performance and apply various software engineering techniques to deploy and scale models.
Keyresponsibilities
MACHINE LEARNING ENGINEERING
- Assist components for Data platform for distributed data processing pipelines and scalable feature stores including data health monitoring and alerts.
- Support components for ML platform to enable distributed model training and evaluations, including model observability and model performance monitoring
- Help end to end Machine Learning Pipeline (i.e MLOps)
- Provide support to data scientists and data engineers to productionize data pipelines and machine learning models, so that various business requirements can be implemented and scaled
- Assist in generating last mile data readiness (for example embeddings) for the Data Scientists so that they can quick move towards value generation i.e. by directly applying models on curated features
ANALYTICS
- Support in application of range of machine-learning techniques in consultation with data scientists and domain experts to enhance models, performance and responsible AI constraints
- Contributes to selection, acquisition, and integration of features (AI focused data components) for analysis.
- Provide support in applying unsupervised ML techniques (like clustering) to data for unknown pattern identification and to run precursory analysis for supervised ML tasks.
DATAMANAGEMENT, MODELLING AND DESIGN
- Assist in Application of exploratory data analysis (EDA), data design, data modelling and quality assurance techniques to establish, modify or maintain highly curated features for the task of AI engineering
- Assists in all of the data engineering needs or assists data engineering towards the goal of project delivery
- Facilitates implementation physical database & data warehouse designs to support feature availability for MDLC
- Assists in providing accessibility, retrievability, security and protection of data in an ethical manner.
PROGRAMMING/SOFTWAREDEVELOPMENT
- Assist in designs, codes, verifies, tests, documents, amends and refactors moderately complex programs/scripts.
- Support development and supports deployment of feature engineering and model training/inferencing code with CI/CD practices in mind
- Assists the builds cloud/on-prem native MDLC templates to orchestrate and channelize development processes for various engineering teams engaged in MDLC
- Facilitates the cross applies model development/deployment in distributed processing and big data paradigms to cover for data volume, velocity, and variety constraints
DATA VISUALIZATION AND STORYTELLING
- Provide support in application of variety visualization techniques and designs the content and appearance of data visuals for storytelling and EDA
- Supports in operationalization and automation of activities for efficient and timely production of data visuals via operationalized dashboards and reports.
- Provide support in communicating results of unsupervised learning techniques (like clustering) to identify and communicate unknown patterns in data
TESTING
- Assist in reviews requirements and specifications and defines test conditions.
- Provide help in designing test cases and test scripts under own direction, mapping back to pre-determined criteria, recording and reporting outcomes.
- Supports the analyses and reports test activities and results.
- Provides help on Identification and reports issues and risks associated with own work.
- Supports in development of embeds unit, integration and regression test cases within CI/CD processes driving MDLC
Keyrelationships
- Global IT
- Respective business function (GOPS, Finance, HR, Brand Marketing, Wholesale/Retail)
- Digital
- Controlling
- HR (Senior) Management
Requisite Education And Experience / Minimum Qualifications
EDUCATION & PROFESSIONAL EXPERIENCE
- Bachelors Degree in Computer Science, Mathematics or similar field; Master's degree is a plus
- 4+ years hands-on experience as a Machine Learning Engineer or similar role, experience with financial or demand planning data is a plus
- Additional internship experience at college preferred but not mandatory
Hard Skills
- Understanding of data structures, data modeling and software architecture
- Experience with production level MLOps - feature engineering, distributed model training, serving & inference, etc.
- Big Data technologies: Apache Kafka, Apache Spark, AWS EMR; Databricks, mlops, exposure to GenAI
- Passionate and ability to write robust code in Python
- Familiarity with machine learning frameworks (like Keras or PyTorch) and ML libraries (like scikit-learn)
- Experience with machine learning algorithms, tools (e.g., AWS Sagemaker, TensorFlow), and natural language processing.
Soft Skills
- Impeccable and to-the-point written and oral communication skills (English)
- High resilience and solution-oriented attitude
- Ability to apply fresh academic ideas/research paper to real world Data Science problem
adidas celebrates diversity, supports inclusiveness and encourages individual expression in our workplace. We do not tolerate the harassment or discrimination toward any of our applicants or employees. We are an equal opportunity employer.
