Barclays Hiring Data Scientist Job | New Opportunity

A Data Scientist analyzes complex data sets to uncover patterns, build predictive models, and provide actionable insights. Proficient in statistical analysis, machine learning, and programming, they collaborate with cross-functional teams to drive data-driven decision-making and solve business problems. Key skills: Python, R, SQL, and data visualization tools.

About Company:

Barclays is a British multinational investment financial institution and economic services business enterprise based in London. Founded in 1690, it operates via the most vital divisions: Barclays UK and Barclays International. The employer provides an extensive style of services which consists of retail banking, credit score gambling cards, agency and funding banking, and wealth control. With a global presence in over forty global locations, Barclays is listed on the London Stock Exchange and is part of the FTSE hundred Index. It is understood for its commitment to innovation, exemplified by using being the number one to release ATMs within the UK.

Role Overview:

Barclays is hiring a Data Scientist for its Pune region, providing a hybrid work surrounding. The function includes designing and imposing facts structure and pipelines, deploying gadget mastering models, and ensuring records are satisfactory and efficient. Key capabilities encompass Python, Docker, AWS, and familiarity with ML Ops tools. Candidates must have experience in model deployment, robust quantitative abilities, and proficiency in massive information technology. Barclays values variety, integrity, and innovation, encouraging employees to be globally linked and impactful. The company emphasizes recognition, provider, excellence, and stewardship, fostering an empowering, tough, and pushed attitude.

Roles and Duties Of Data Scientist Job in Pune:

Data Scientists play a pivotal function in leveraging facts to drive selection-making and innovation across numerous industries. Here are the pinnacle five roles and responsibilities of a Data Scientist.

Data Collection and Cleaning:

Data Scientists are chargeable for collecting statistics from more than one resource and making sure it’s exceptional. This includes figuring out the applicable information, scraping or obtaining it, and cleaning it by doing away with inaccuracies and inconsistencies.

Data Analysis and Interpretation:

They perform thorough analyses of the usage of statistical techniques and gadgets getting to know algorithms to extract significant insights. This includes exploring information, figuring out styles, and decoding results to inform business techniques and choices.

Model Building and Deployment:

Data Scientists develop predictive fashions and device-studying algorithms to clear up complex troubles. They pick suitable modeling strategies, teach fashions, validate their overall performance, and install them into production environments.

Visualization and Communication:

Communicating findings efficaciously is critical. Data Scientists create visualizations and dashboards to offer insights certainly to non-technical stakeholders. They additionally put together unique reports and presentations to help facts-driven choice-making.

Continuous Learning and Improvement:

The subject of statistics science is unexpectedly evolving. Data Scientists need to live up to date with today’s tools, technology, and methodologies. They constantly refine fashions, optimize tactics, and put into effect new techniques to improve accuracy and efficiency.

Skills Required For Data Scientist:

Statistical Analysis and Mathematics:

A strong foundation in information and arithmetic is vital for statistics scientists. This includes expertise in probability, hypothesis checking out, regression, and various statistical techniques to research and interpret complicated statistics.

Programming:

Proficiency in the programming languages of Python and R is vital. These languages are extensively used for statistics manipulation, statistical analysis, gadget learning, and visualization. Knowledge of SQL is also critical for database management and querying.

Machine Learning and AI:

Data scientists should apprehend machine studying algorithms and techniques, which include supervised and unsupervised studying, deep getting-to-know, and natural language processing. This allows them to construct predictive fashions and uncover insights from big datasets.

Data Wrangling and Preprocessing:

The potential to clean, remodel, and arrange uncooked records into a usable layout is important. Data scientists regularly spend a large amount of time making ready data, dealing with missing values, and ensuring records are satisfactory for evaluation.

Data Visualization and Communication:

Effective communique of findings through records visualization tools like Tableau, Power BI, or Matplotlib is critical. Data scientists must present complex outcomes surely and compellingly to stakeholders, enabling information-pushed choice-making.

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