Data Science - Lead Data Scientist Job in Jet2 Travel Technologies Pvt. Ltd.

For this role, please see some pointers below. We will assess you 3 themes technical skills, smartness and cultural fitment Cultural fitment our probe will cover the following aspect Attitude, especially to understand your motivations and behaviours in the fast-paced ecosystem Your approach to working with colleagues and customers Your approach to dealing with adversity Your career aspirations and your plans to achieve these aspirations. Smartness - our probe will cover the following aspects Confidence exhibited throughout the interview process Clarity of thoughts and deftness in articulation of thoughts Ability to handle pressure Skills our probe will cover the following aspects Thorough understanding of recent/previous project. Knowledge of new trends and technology You might be asked to design, write or review code. So, please have access to machine with right software A lead data scientist would lead a team of data science team Lead will be responsible for delivering & managing day-to-day activities The successful candidate will be highly numerate with a statistical background, experienced in using R, Python or similar statistical analysis package Your ability to rise above group think and see beyond the here and now is matched only by your intellectual curiosity. Strong SQL skills and the ability to create clear data visualisations in tools such as Tableau or Power BI will be essential. They will also have experience in developing and deploying predictive models using machine learning frameworks and worked with big data technologies. As we aim to realise the benefits of cloud technologies, some familiarity with cloud platforms available for data science and storage would be desirable. You will be skilled in gathering data from multiple sources and in multiple formats with knowledge of data warehouse design, logical and physical database design and challenges posed by data quality. Demonstrable experience in cleansing and enriching of data, interpreting business requirements in terms of data architecture, database technology and data model design will also be key.