Description

We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (20000+ experts across 33 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in! Requirements: Strong Experience in Python for Data Science, Data Science on AWS, Data Science solutions in Banking domain, Communication and Collaboration, Statistics & Probability, Data visualization and Cloud architecture Ability to design and implement workflows of Linear & Logistic Regression, Ensemble Models (Random Forest, Boosting) using R/Python. Demonstrable competency in Probability & Statistics, ability to use ideas of Data Distributions, Hypothesis Testing, and other Statistical Tests. Demonstrable competency in Data Visualisation using the Python/R Data Science Stack. Hands-on experience in using statistical and analytical techniques to solve clients' problems. Hands-on experience in solving regression, prediction, classification, clustering, neural networks, and Bayesian problems. Advanced knowledge of statistical techniques, machine learning algorithms, Bayesian Models, data mining, and text mining. Experience in handling large datasets on cloud and on-premises setup, using distributed computing. Able to understand various data structures and common methods in data transformation. Strong Programming background and expertise in building models in languages like Python, R, Scala, etc. Good knowledge of visual techniques for data analysis and presentation skills Strong troubleshooting skills in different disparate technologies and environments Responsibilities: Understanding the client's business use cases and technical requirements and being able to convert them into a technical design that elegantly meets the requirements Come up with solutions for Data Science business problems, implementation, and review code. Come up with use cases and accelerators. Understand business problems and propose solutions. Guide team with best practices. Design solutions on cloud and on-premise and present solutions to clients. Identifying different solutions and being able to narrow down the best option that meets the clients requirements. Responsible for the architecture and design of data science platforms & service capabilities by envisioning and executing strategies that will enable and leverage modern data science capabilities. Implementation of sophisticated analytics programs, machine learning, and statistical methods to prepare enterprise data for use in predictive and prescriptive modeling. Accountable for the data science platform design in addition to the use of case-based application solution design. Utilize a blend of contemporary and traditional data science techniques, applied to both structured and unstructured data sets. Work closely with other Data and Analytics Teams, inclusive of data analytics, data warehousing, and data engineering teams in creating big data applications through the utilization of structured and unstructured data, designing optimal data architecture, and experimenting with new machine learning techniques. Responsible for consistently identifying and monitoring key business risks and realizing the data needs of the business. Carrying out POCs to make sure that suggested designs/technologies meet the requirements.

Role and Responsibilities

• We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (20000+ experts across 33 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in! Requirements: Strong Experience in Python for Data Science, Data Science on AWS, Data Science solutions in Banking domain, Communication and Collaboration, Statistics & Probability, Data visualization and Cloud architecture Ability to design and implement workflows of Linear & Logistic Regression, Ensemble Models (Random Forest, Boosting) using R/Python. Demonstrable competency in Probability & Statistics, ability to use ideas of Data Distributions, Hypothesis Testing, and other Statistical Tests. Demonstrable competency in Data Visualisation using the Python/R Data Science Stack. Hands-on experience in using statistical and analytical techniques to solve clients' problems. Hands-on experience in solving regression, prediction, classification, clustering, neural networks, and Bayesian problems. Advanced knowledge of statistical techniques, machine learning algorithms, Bayesian Models, data mining, and text mining. Experience in handling large datasets on cloud and on-premises setup, using distributed computing. Able to understand various data structures and common methods in data transformation. Strong Programming background and expertise in building models in languages like Python, R, Scala, etc. Good knowledge of visual techniques for data analysis and presentation skills Strong troubleshooting skills in different disparate technologies and environments Responsibilities: Understanding the client's business use cases and technical requirements and being able to convert them into a technical design that elegantly meets the requirements Come up with solutions for Data Science business problems, implementation, and review code. Come up with use cases and accelerators. Understand business problems and propose solutions. Guide team with best practices. Design solutions on cloud and on-premise and present solutions to clients. Identifying different solutions and being able to narrow down the best option that meets the clients requirements. Responsible for the architecture and design of data science platforms & service capabilities by envisioning and executing strategies that will enable and leverage modern data science capabilities. Implementation of sophisticated analytics programs, machine learning, and statistical methods to prepare enterprise data for use in predictive and prescriptive modeling. Accountable for the data science platform design in addition to the use of case-based application solution design. Utilize a blend of contemporary and traditional data science techniques, applied to both structured and unstructured data sets. Work closely with other Data and Analytics Teams, inclusive of data analytics, data warehousing, and data engineering teams in creating big data applications through the utilization of structured and unstructured data, designing optimal data architecture, and experimenting with new machine learning techniques. Responsible for consistently identifying and monitoring key business risks and realizing the data needs of the business. Carrying out POCs to make sure that suggested designs/technologies meet the requirements.