Data Science (Lead)

Responsibilities

·         Partners with lines of business to understand business problems and translate them into identifiable machine learning problems which can be delivered as technical solutions and actionable recommendations across the organization

·         Coordinate with business teams to monitor outcomes and refine/ improve the machine learning models

·         Work across the spectrum of statistical modelling including supervised, unsupervised, & deep learning techniques to apply the right level of solution to the right problem

·         Collaborate & share best practices with data and software engineers to enable consistent deployment of high-quality models that will scale across the company’s ecosystem

·         Own the code review process to ensure stringent coding guidelines are met by other data scientists & data engineers

·         Mentor & lead Data Science engineers in choosing the right machine learning approach/ models & utilizing open source languages such as R, Python, etc.

·         Lead data mining and collection procedures for all business use cases and guarantee data quality and integrity

·         Utilize Data visualization tools to deliver insights to stakeholders and present technical solutions to non-technical audience in a simple and clear manner

·         Track emerging developments w.r.t technology, tools, practices, etc in the open source, data science/machine learning/chatbot domain and leverage same for Schneider Electric

·         Build frameworks leveraging APIs to industrialize AI models across the organization

·         Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)

Requirements and Skills

·         You should have bachelor’s or Master’s degree in Computer Science, Statistics or Mathematics, Informatics, Information Systems or another quantitative field

·         You should have 7+ years of experience in solving real life complex business problems using machine learning. Hands-on experience in deploying these machine learning solutions to production is mandatory.

·         In depth understanding of NLP, LLM, Generative AI, RAG application development.

·         In-depth understanding and modeling experience in supervised, unsupervised, reinforcement and deep learning models; hands-on knowledge of data wrangling, data cleaning/ preparation, dimensionality reduction is required

·         Knowledge of vector algebra, statistical and probabilistic modelling is highly desirable

·         Exploratory data analysis and hypothesis testing to identify ML opportunities is a plus

·         Experience in major machine learning frameworks such as Pytorch, Scikit-Learn, Tensorflow, Pandas, SparkML etc.

·         Fluency in programming skills such as Python, R, or other equivalent languages

·         Familiarity with databases like MySQL, Oracle, SQL Server, NoSQL, etc. is desirable

·         Experience working with Amazon SageMaker or Azure ML Studio for deployments is required

·         Experience in data visualization software such as Tableau, ELK, etc is a plus

·         Strong analytical and critical thinking skills. You should also have a business mindset, swift to identify risk situations and opportunities, and able to generate creative solutions to business problems

·         Effective communication skills (written and verbal) to properly articulate complicated statistical models/ reports to management and other IT development partners