Senior, Data Scientist

Job Profile - Lead, Data Scientist

We are looking for a passionate ML /AI Lead Data Scientist with demonstrated thought-leadership & proven ability to lead the development of analytics-focused products and utilizing cutting edge machine learning, natural language processing, and mathematical techniques to drive powerful business results. You will have the opportunity to work with a wide range of stakeholders and functional teams to help them discover insights hidden in data sets and enable stakeholders to improve business. AI ethics, fairness and secure development practices are also our focus areas.

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 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

About Us

Schneider Electric™ creates connected technologies that reshape industries, transform cities and enrich lives. Our 144,000 employees thrive in more than 100 countries. From the simplest of switches to complex operational systems, our technology, software and services improve the way our customers manage and automate their operations. Help us deliver solutions that ensure Life Is On everywhere, for everyone and at every moment:

Great people make Schneider Electric a great company.

We seek out and reward people for putting the customer first, being disruptive to the status quo, embracing different perspectives, continuously learning, and acting like owners. We want our employees to reflect the diversity of the communities in which we operate. We welcome people as they are, creating an inclusive culture where all forms of diversity are seen as a real value for the company. We’re looking for people with a passion for success — on the job and beyond.

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