Data Scientist

ABB’s interview process typically starts with an online assessment that tests quantitative aptitude, programming, and basic data‑science concepts. This is followed by a technical interview where candidates discuss past projects, solve case‑based ML problems, and may be asked to write code on a whiteboard or shared editor. The final HR round evaluates cultural fit, communication skills and career aspirations. Overall difficulty is moderate to high; thorough preparation on statistics, Python coding and business case analysis is essential.

Round 1: Online Assessment (aptitude, coding, data‑analytics) → Round 2: Technical Interview (ML concepts, case studies, coding) → Round 3: HR Interview (fit, motivation, salary discussion)

ABB promotes a collaborative and inclusive work environment where engineers and analysts are encouraged to experiment and innovate. Employees enjoy a balanced work‑life schedule, robust learning resources, and clear career progression pathways within a globally respected brand.

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or related fields; minimum 60% aggregate (or CGPA 6.0/10); graduating batch 2025 or 2026; no active backlogs at the time of joining; strong analytical mindset and willingness to learn.

ABB is a global leader in electrification, robotics, automation and motion technologies, serving customers in utilities, industry, transport and infrastructure. With a heritage spanning more than 130 years, ABB combines deep engineering expertise with cutting‑edge digital solutions, enabling a more sustainable and efficient future. The company operates in over 100 countries and invests heavily in research, development and talent, fostering an environment where innovation thrives. As a technology‑driven organization, ABB places data at the core of its decision‑making, using advanced analytics to optimise processes, reduce downtime and create new business models. The Data Scientist role at ABB is positioned within the People Analytics and broader Business Intelligence teams, reporting to senior analytics leadership. The incumbent will translate complex business challenges into data‑driven solutions, building predictive models, risk‑identification frameworks and actionable dashboards that empower stakeholders across the enterprise. This role offers exposure to cross‑functional projects, ranging from manufacturing efficiency to workforce analytics, and provides a platform to influence strategic initiatives at a global scale. Key responsibilities include: 1. Develop advanced statistical and machine‑learning models to generate actionable insights. 2. Create complex data models for early risk identification and prevention. 3. Analyse large datasets, prepare reports and presentations to support operational efficiency and decision‑making. 4. Translate business requirements into technical specifications and identify required data sources. 5. Lead end‑to‑end analytics projects, from requirement gathering to insight delivery. 6. Manage strategic data‑process mining developments and roll‑out analytics use cases to end users. 7. Collaborate with cross‑functional and global teams on initiatives and projects. 8. Conduct business‑value workshops to identify process inefficiencies and improvement opportunities. 9. Design, develop, implement, test and validate use cases, reports and dashboards. 10. Leverage programming languages and platforms such as Python, R, SQL, Celonis and data lakes, and apply NLP, deep learning and AI techniques to interpret unstructured data. Technical stack: Python, R, SQL, Pandas, Scikit‑learn, TensorFlow/PyTorch, NLP libraries (spaCy, NLTK), data‑visualisation tools (PowerBI, Tableau), Celonis, Azure/AWS data lakes. Growth path: Starting as an Analyst‑level Data Scientist, high performers can progress to Senior Data Scientist, Lead Analytics Engineer, and eventually to Analytics Manager or Head of Data Science, with opportunities to lead global initiatives and mentor junior talent. Why join ABB? The company offers a vibrant, innovation‑centric culture, competitive compensation, and exposure to industry‑leading technologies. Employees benefit from continuous learning programmes, international mobility, and the chance to work on projects that have a tangible impact on sustainability and digital transformation worldwide.