Machine Learning Analyst - Porto, Portugal - Adidas

Adidas

Empresa verificada

Porto, Portugal

há 1 semana

Postado por:

João Santos

Recrutador de beBee

Descrição

Purpose & Overall Relevance for the Organization:

Key Responsibilities:

Machine Learning Engineering
Builds components for Data platform for distributed data processing pipelines and scalable feature stores including data health monitoring and alerts.
Builds components for ML platform to enable distributed model training and evaluations, including model observability and model performance monitoring
Designs end to end Machine Learning Pipeline (i.e MLOps)
Works with data scientists and data engineers to productionize data pipelines and machine learning models, so that various business requirements can be implemented and scaled
Analytics
Applies a range of machinelearning techniques in consultation with data scientists and domain experts to enhance models within the explainable AI(XAI), performance and responsible AI constraints
Selects, acquires, and integrates features (AI focused data components) for analysis.
Applies unsupervised ML techniques (like clustering) to data for unknown pattern identification and to run precursory analysis for supervised ML tasks.
Data management, modelling and design
Applies 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
Fills in for all the data engineering needs or assists data engineering towards the goal of project delivery
Implements 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/software development
Designs, codes, verifies, tests, documents, amends, and refactors moderately complex programs/scripts.
Develops and deploys feature engineering and model training/inferencing code with CI/CD practices in mind
Builds cloud/onprem native MDLC templates to orchestrate and channelize development processes for various engineering teams engaged in MDLC
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
Applies a of variety visualization techniques and designs the content and appearance of data visuals for storytelling and EDA
Operationalizes and automates activities for efficient and timely production of data visuals via operationalized dashboards and reports.
Communicates results of unsupervised learning techniques (like clustering) to identify and communicate unknown patterns in data
Testing
Reviews requirements and specifications and defines test conditions.
Designs test cases and test scripts under own direction, mapping back to predetermined criteria, recording and reporting outcomes.
Analyses and reports test activities and results.
Identifies and reports issues and risks associated with own work.
Embeds unit, integration and regression test cases within CI/CD processes driving MDLC
If required: People Management / Resource Management:
May be involved and gives some input on hiring Transition decisions
Ensures appropriate leadership skills are present at every level through creating a motivational and supportive work environment in which employees are coached, trained and provided with career opportunities through development
Allocates the different work to the respective employees considering experience, complexity, workload and organizational efficiency
Continuously monitors and evaluates team workload and organizational efficiency with the support of IT systems, data and analysis and team feedback and makes appropriate changes to meet business needs.
Provides team members/direct reports with clear direction and targets that are aligned with business needs and GIT objectives.
Key Relationships:
Global IT
Respective business function (GOPS, Finance, HR, Brand Marketing, Wholesale/Retail)
Digital
Advanced Analytics and Data Science
Requisite Education and Experience / Minimum Qualifications:
Education & Professional experience
Bachelors Degree in Computer Science, Mathematics or similar field; Master's degree is a plus
1,5 years' handson experience as a Machine Learning Engineer or similar role
Hard skills
Understanding of data structures, data modeling and software architecture
Experience with production level MLOps feature engineering, distributed model training, serving & inference, etc.
Deep knowledge algorithms and Big Data technologies: Apache Kafka, Apache Spark, AWS EMR; Databricks is a plus
Passionate and ability to write robust code in Python, R, Java
Familiarity with machine learning frameworks (like Keras or PyTorch) and ML libraries (like scikitlearn)
Experience with machine learning algorithms, tools (e.g., MLflow, AWS Sagemaker, TensorFlow), deep learning and/or natural language processing.
Soft skills
Impeccable and tothep