Job Description

W ABB pomagamy przemys?owi wyprzedza? konkurencj? - dzia?a? sprawniej i ekologicznie. U nas post?p to standard - dla Ciebie, Twojego zespo?u i ca?ego ?wiata. Jako globalny lider dajemy Ci to, czego potrzebujesz, by to osi?gn??. Nie zawsze b?dzie ?atwo, rozwoj wymaga determinacji. Ale w ABB nigdy nie b?dziesz dzia?a? sam. Zarz?dzaj tym, co porusza ?wiat.


Stanowisko to podlega:


Data Analytics and BI Manager

As a Senior Data Scientist you will help build the next generation of AI/ML-enabled solutions that underpin our strategic vision of digitalizing industrial projects and operational risk. You will apply your expertise in machine learning, cloud platforms (especially Snowflake and Azure), agile delivery (using Azure DevOps), and software development practices, to deliver scalable, robust, production-ready systems across the globe.


You will play a pivotal role in positioning our team as the central AI Hub for the P&A division - consulting and co-planning with platform teams, defining MLOps and governance standards, and guiding complex AI/ML projects from ideation to production.

Key Responsibilities:

Lead end-to-end ML/AI initiatives: problem definition (e.g., churn modelling, customer retention, risk analytics, dynamic process modelling), data exploration, feature engineering, model development, validation, deployment and monitoring in production. Design and build data pipelines and ML workflows leveraging Snowflake and Azure (data engineering, modelling, orchestration). Snowflake experiences are

mandatory

. Develop and deploy machine learning models at scale in cloud environments (Azure native services preferred). Apply agile frameworks and tools (Azure DevOps) to manage sprints, backlog, CI/CD, versioning, testing, and deployment of ML solutions. Collaborate with cross-functional teams (platform, data engineering, software development, business stakeholders, vendors) to ensure alignment with business objectives and delivery roadmap. Establish and promote best practices in MLOps, model governance, monitoring, versioning, retraining, and operationalization of AI solutions. Bring software development discipline into data science work: code reviews, modular design, unit/integration testing, documentation, UI/UX for ML tools. (Preferred) Build or influence user interfaces for ML/AI applications (for example using frameworks like Streamlit or notebooks/dashboards or using Databricks). Mentor junior data scientists and promote a culture of learning and innovation across the team. Communicate results clearly to both technical and non-technical stakeholders, translating data science outcomes into actionable business insights.

Key requirements (mandatory):

Bachelor's degree (or higher) in Computer Science, Physics, Statistics, Mathematics, Engineering or a related quantitative field. Minimum

4+ years

of hands-on experience in machine learning/AI within industry -- designing and deploying ML/AI projects. Strong practical expertise with Snowflake (data warehousing, Snowpark, stored procedures, dynamic tables etc.) this is mandatory. Proven experience on Azure cloud platform (Azure ML, Azure Data Factory, Azure Synapse etc.). Solid programming skills (Python, SQL) and software development experience (code versioning, testing, modular design). Experience in Agile practices for AI development. Experience working in Agile delivery frameworks and tools -- particularly Azure DevOps (CI/CD pipelines, sprint planning, backlog management). Demonstrated track record of working on large scale ML projects: e.g., customer-retention/churn modelling, risk analytics, dynamic process modelling in an industrial/enterprise context. Understanding of MLOps lifecycle: deployment, monitoring, retraining, production support. Excellent communication skills and ability to work collaboratively across teams and geographies.

Desirable:

Experience building UI/UX components for ML solutions (e.g., using Streamlit, Dash, Databricks notebooks). Experience with Databricks environment (notebooks, clusters, MLflow, Spark). Experience in industrial domains (manufacturing, automation, process industries) or working in large-scale global organizations. Experience working with unstructured data or advanced AI (e.g., dynamic modelling, reinforcement learning).

Competency

Strategic thinking: able to see the business context and align technical work with strategic priorities. Technical leadership: able to influence, mentor, and drive best practices. Delivery orientation: strong sense of ownership to take projects from idea to value realization. Continuous learning mindset keeps abreast of latest ML/AI/cloud trends and drives improvements.

Doceniamy ro?norodno?? do?wiadcze?. Czy to mo?e by? Twoja historia? Aplikuj ju? dzi? lub odwied? stron? www.abb.com, aby dowiedzie? si? wi?cej o nas i pozna? wp?yw naszych rozwi?za? na ca?ym ?wiecie.