Description

The Lead Data Science (similar to an Engagement Manager) is the Project leader who will lead the areas of communicating with the clients and advising them , Identifying and phrasing problem statement , identifying data needed to solve the problem , designing mechanism to solve the data problem and delivering the working solution . He or she will be the key person interacting with the client teams and vendors for the project and will also direct the analysts working on the projects. He will be reporting to the Head of Data Science team and seek timely directives and help from him for all parts of the project What You ll Do Dealing with the Operational teams from client side. This may include interactions with the Operations, Sales, Marketing and IT heads from client side. It includes following key aspects: Communicating and understanding clearly what are the business-data challenge. Culturally sensitizing the client side for being open to sharing and using data for solving problems and letting machines take certain decisions using AI. Knowing the art of identifying what data needs to be extracted/ used for solving a particular challenge. Designing a consumption layer for the client to consume the final delivered solution. Explaining the clients what the solution is doing behind the curtains in a very simplified manner. Dealing with Data Science Vendors: Summarizing the business-data challenge to them and giving clear deliverables. Timely interfacing with the vendors in resolving their asks and queries. Testing and verifying their deliverables and refining them. Maintaining a healthy relationship with key vendors Managing internal project team and Science behind projects: Managing analysts on the project work distribution and timeline planning for the projects, training and guiding the analysts. Designing the frameworks in data sciences (R, Python, Azure ML, Amazon ML, Google ML, Spark etc) to be used for relevant projects. Choice of Data and Machine learning algorithms to solve a particular problem. Creating Data Strategy roadmaps for businesses in the long term. Creating post project delivery way forward for ensuring sustainability of data science solutions delivered. Being aware of the world-class and most recent advances in data science areas and having ready references to the best methods in the data science fraternity. Displaying thought leadership on social media on all things data science Proactively identifying applications and ideas of using data science across Mahindra Finance Helping other teams at MMFSL for consultations on data expertise What We Value High degree of Emotional Intelligence in managing relationships with client side business leaders. Most of the data challenges will involve dealing with department heads from Marketing, IT, FInance etc and would need convincing the client side of what data needs to be shared and how it will be used. There is a need to have a skill of selling the use of Machine Learning in conjunction with humans. The person needs to have a personality, composure and body language equivalent to that of a consultant. Knowledge of key Data Science tools like: Python based machine learning, Cloud based ML like Azure ML. Understanding of what algorithms need to be used to solve different classes of data problems. Consulting skills like: understanding business challenges, framing problem statements, delivering concise reports/ decks for CXO level consumption. Most important: Work experience of leading and delivering Data Science projects to clients. The work may include problems in :Financial analytics, Lending algorithms, MArketing Analytics, Semantic analysis, Social media Analytics, Operational problems like inventory and supply chain optimization, Revenue/pricing/product analytics etc

Role and Responsibilities

• The Lead Data Science (similar to an Engagement Manager) is the Project leader who will lead the areas of communicating with the clients and advising them , Identifying and phrasing problem statement , identifying data needed to solve the problem , designing mechanism to solve the data problem and delivering the working solution . He or she will be the key person interacting with the client teams and vendors for the project and will also direct the analysts working on the projects. He will be reporting to the Head of Data Science team and seek timely directives and help from him for all parts of the project What You ll Do Dealing with the Operational teams from client side. This may include interactions with the Operations, Sales, Marketing and IT heads from client side. It includes following key aspects: Communicating and understanding clearly what are the business-data challenge. Culturally sensitizing the client side for being open to sharing and using data for solving problems and letting machines take certain decisions using AI. Knowing the art of identifying what data needs to be extracted/ used for solving a particular challenge. Designing a consumption layer for the client to consume the final delivered solution. Explaining the clients what the solution is doing behind the curtains in a very simplified manner. Dealing with Data Science Vendors: Summarizing the business-data challenge to them and giving clear deliverables. Timely interfacing with the vendors in resolving their asks and queries. Testing and verifying their deliverables and refining them. Maintaining a healthy relationship with key vendors Managing internal project team and Science behind projects: Managing analysts on the project work distribution and timeline planning for the projects, training and guiding the analysts. Designing the frameworks in data sciences (R, Python, Azure ML, Amazon ML, Google ML, Spark etc) to be used for relevant projects. Choice of Data and Machine learning algorithms to solve a particular problem. Creating Data Strategy roadmaps for businesses in the long term. Creating post project delivery way forward for ensuring sustainability of data science solutions delivered. Being aware of the world-class and most recent advances in data science areas and having ready references to the best methods in the data science fraternity. Displaying thought leadership on social media on all things data science Proactively identifying applications and ideas of using data science across Mahindra Finance Helping other teams at MMFSL for consultations on data expertise What We Value High degree of Emotional Intelligence in managing relationships with client side business leaders. Most of the data challenges will involve dealing with department heads from Marketing, IT, FInance etc and would need convincing the client side of what data needs to be shared and how it will be used. There is a need to have a skill of selling the use of Machine Learning in conjunction with humans. The person needs to have a personality, composure and body language equivalent to that of a consultant. Knowledge of key Data Science tools like: Python based machine learning, Cloud based ML like Azure ML. Understanding of what algorithms need to be used to solve different classes of data problems. Consulting skills like: understanding business challenges, framing problem statements, delivering concise reports/ decks for CXO level consumption. Most important: Work experience of leading and delivering Data Science projects to clients. The work may include problems in :Financial analytics, Lending algorithms, MArketing Analytics, Semantic analysis, Social media Analytics, Operational problems like inventory and supply chain optimization, Revenue/pricing/product analytics etc