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    What Is The Hiring Process For Data Scientists At Hitachi ABB Power Grids

    What Is The Hiring Process For Data Scientists At Hitachi ABB Power Grids

    Discover the rigorous hiring process for data scientists at Hitachi ABB Power Grids. Learn how to prepare for your application today!

    What Is The Hiring Process For Data Scientists At Hitachi ABB Power Grids
    Srishti DeorasΒ·Β·Updated DECEMBER 24, 2024, 5:30 AM
    5 min

    The Data Science group in Hitachi ABB Group is embedded with several business units, services and functions, where they get to contribute and lead projects creating world-leading applied analytics and machine learning technologies. With a strong focus of data science on product development, R&D, manufacturing, quality & OPEX, shared services (Finance, Controlling & Planning, SCM, HR), the company has a flat organisational structure. 

    "While hiring data scientists, we at Hitachi ABB Power Grids believe in looking at it in two different ways - first, data science getting embedded internally for the betterment of our process & products; and second, to focus on how we can leverage it to better position our products and services for our customers," shared Raja Radhakrishnan, Human Resource Director, Hitachi ABB Power Grids India. The company, therefore, follows a stringent data science recruitment process. He shared the process with Analytics India Magazine as below. 

    Skill Sets For Data Scientists At Hitachi ABB Group 

    Radhakrishnan shares that while recruiting data scientists, domain-specific know-how is of utmost importance to them. "We also look for their experience in applying data science methods to solve practical problems which might include predictive analytics, machine learning, statistical analysis, probabilistic modelling, statistical process monitoring, time-series modelling, data mining etc.," he said.

    In terms of educational background, they look for bachelors, masters or doctorate in domains such as engineering, computer science or any related field. Additional exposure in programming languages such as Python, R, Julia, MATLAB, and certification in data science is an added advantage. 

    So, what weighs more β€” educational background or skills while hiring for data science roles? Radhakrishnan shares that skills & relevant experience are what they focus on. However, he further added that it doesn't mean that the candidates' educational background is ignored. "The candidates' expertise in using the data science skill sets; their analytical ability, their attitude towards continuous learning – all matter," he said. 

    Data Scientist Interview Process

    The company looks for hiring data science candidates who are passionate about identifying and solving real-world challenges (new problems & new improvement areas) for internal and external customers using data analytics, machine learning and/or artificial intelligence technologies. Radhakrishnan further shared that they look for candidates who are willing to learn, validate research findings through simulation, prototyping and/or demonstration, as applicable and help identify & validate business propositions to facilitate technology commercialisation in the organisation.

    To ensure that they hire an ideal data scientist, the company follows thorough technical rounds of discussions, case studies on data science problem statements & improvement areas to evaluate the analytical capabilities. It also focuses on personality assessments to evaluate the candidates' competency fitment to the role followed by cross-functional panel interviews.

    "We follow a combination of technical & behavioural interviews based on instances, their experience on practical problems solved at their domain, the techniques & tools they have used, coding etc.," said Radhakrishnan. 

    He added that apart from scouting for the cream of talent from university hiring, they use LinkedIn search, direct applications on Hitachi ABB Power Grids' website & recruitment search agencies. "We're open to internships and conversion of interns too for the best of talent," he said. 

    However, recruiting data scientists is not easy. It comes with its own set of challenges. Radhakrishnan shared that the right candidates are scarce, making it difficult to find the perfect combination of data science expertise & domain knowledge.

    "For instance, we've found it difficult to source candidates who are exceptional in power automation backed with his / her exposure in data science. Same is our experience when we've to scout for candidates whose domain expertise is in manufacturing quality with data science and desirable IoT experience," he added. 

    Adding a note on the selection process, Radhakrishnan stated that at the selection level, Hitachi ABB Power Grids India ensures that the candidates' skill sets along with experience are given equal importance. "They get to articulate themselves through technical and behavioural interviews, topping up with case studies & assessments to weed out any possible mistakes that can happen," he shared.

    Data scientists can look for job openings in the company through Hitachi ABB Power Grids website, LinkedIn job postings.

    Growth Opportunities Of Data Scientists At Hitachi ABB

    Hitachi ABB Power Grids is a matrix organisation and offers growth opportunities for data scientists across geographies & different business units. "They can grow vertically and horizontally based on their interest rising up to roles where they can be leading business verticals and function," shared Radhakrishnan.

    Radhakrishnan believes that data science will always remain an integral part of the culture & economy and is continuously evolving. It's now being used across sectors, be it manufacturing, services, healthcare or FMCG. He, therefore, advises candidates to understand and develop core competencies in the data science domain. "With the explosion of data & IoT, there is a large amount of data available today and utilising them in the right way can spell success and failure for any organisation," he said on a concluding note. 

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