Major Duties & Responsibilities
•  Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions 
•  Create Proof of concepts (POCs) / Minimum Viable Products (MVPs), then guide them through to production deployment and operationalization of projects 
•  Influence machine learning strategy for Digital programs and projects
•  Make solution recommendations that appropriately balance speed to market and analytical soundness
•  Explore design options to assess efficiency and impact, develop approaches to improve robustness and rigor 
•  Develop analytical / modelling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow)
•  Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations.
•  Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
•  Create algorithms to extract information from large, multiparametric data sets.
•  Deploy algorithms to production to identify actionable insights from large databases.
•  Compare results from various methodologies and recommend optimal techniques. 
•  Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
•  Develop and embed automated processes for predictive model validation, deployment, and implementation 
•  Work on multiple pillars of AI including cognitive engineering, conversational bots, and data science
•  Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment 
•  Lead discussions at peer review and use interpersonal skills to positively influence decision making
•  Provide thought leadership and subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices
•  Facilitate cross-geography sharing of new ideas, learnings, and best-practices

Required Qualifications

•  Bachelor of Science or Bachelor of Engineering at a minimum.
•  4-6 years of work experience as a Data Scientist
•  A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of a project
•  Advanced skills with statistical/programming software (e.g., R, Python) and data querying languages (e.g., SQL, Hadoop/Hive, Scala)
•  Good hands-on skills in both feature engineering and hyperparameter optimization
•  Experience producing high-quality code, tests, documentation
•  Experience with Microsoft Azure or AWS data management tools such as Azure Data factory, data lake, Azure ML, Synapse, Databricks
•  Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and / or deep learning methodologies
•  Proficiency in statistical concepts and ML algorithms
•  Good knowledge of Agile principles and process 
•  Ability to lead, manage, build, and deliver customer business results through data scientists or professional services team
•  Ability to share ideas in a compelling manner, to clearly summarize and communicate data analysis assumptions and results
•  Self-motivated and a proactive problem solver who can work independently and in teams

Major Duties & Responsibilities
•  Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions 
•  Create Proof of concepts (POCs) / Minimum Viable Products (MVPs), then guide them through to production deployment and operationalization of projects 
•  Influence machine learning strategy for Digital programs and projects
•  Make solution recommendations that appropriately balance speed to market and analytical soundness
•  Explore design options to assess efficiency and impact, develop approaches to improve robustness and rigor 
•  Develop analytical / modelling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow)
•  Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations.
•  Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
•  Create algorithms to extract information from large, multiparametric data sets.
•  Deploy algorithms to production to identify actionable insights from large databases.
•  Compare results from various methodologies and recommend optimal techniques. 
•  Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
•  Develop and embed automated processes for predictive model validation, deployment, and implementation 
•  Work on multiple pillars of AI including cognitive engineering, conversational bots, and data science
•  Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment 
•  Lead discussions at peer review and use interpersonal skills to positively influence decision making
•  Provide thought leadership and subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices
•  Facilitate cross-geography sharing of new ideas, learnings, and best-practices

Required Qualifications

•  Bachelor of Science or Bachelor of Engineering at a minimum.
•  4-6 years of work experience as a Data Scientist
•  A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of a project
•  Advanced skills with statistical/programming software (e.g., R, Python) and data querying languages (e.g., SQL, Hadoop/Hive, Scala)
•  Good hands-on skills in both feature engineering and hyperparameter optimization
•  Experience producing high-quality code, tests, documentation
•  Experience with Microsoft Azure or AWS data management tools such as Azure Data factory, data lake, Azure ML, Synapse, Databricks
•  Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and / or deep learning methodologies
•  Proficiency in statistical concepts and ML algorithms
•  Good knowledge of Agile principles and process 
•  Ability to lead, manage, build, and deliver customer business results through data scientists or professional services team
•  Ability to share ideas in a compelling manner, to clearly summarize and communicate data analysis assumptions and results
•  Self-motivated and a proactive problem solver who can work independently and in teams

B.Tech/M.tech, B.Sc/M.sc, BCA/MCA