Senior Data Scientist

About This Opportunity
Are you a data scientist or machine learning engineer who enjoys solving real-world problems by applying cutting edge Gen AI/ML algorithms? Are you passionate about working with customers to solve their business challenges? Can you explain complex machine learning concepts in simple business terms? If the answer is “yes”, then we have a position for you!

What You Will Do

Deliver data science and/or machine learning professional services to the customers. This will entail working closely with cross-functional teams to gather requirements, preprocess, and analyze data to develop data-driven solutions.
Support customers by helping to build custom data models, transformation, scorers/optimization loss functions/metrics that can be leveraged with Driverless AI and help improve training performance for the customer use cases/domains.
Ability to design and run training sessions for our customers in data science concepts as well as using products. Training will need to be able to be delivered to audiences with varying data science skills and abilities.
Help customers understand the nuances of data science, expert settings of Driverless AI and open source, and the strategies of experiment tuning that you would use to incrementally improve model performance. This also includes explaining model performance, model interpretability, and post deployment model monitoring concepts so as to ensure the business is making the right decisions on the model predictions.
Participate in the development of ML solutions and AI applications for internal use, contributing to the advancement of our technology stack.

What We Are Looking For

Proven experience in data science, machine learning, and AI with a minimum of 2 years of hands-on experience.
Experience with solving machine learning problems using products (plus), Python, R
Knowledge and experience of using a variety of machine learning techniques (supervised/unsupervised, clustering, decision tree learning, neural networks, etc.) and their real-world advantages/drawbacks/tuning techniques.
Knowledge and experience of using advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) for practical applications.
Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
Experience of visualizing and presenting (EDA) to stakeholders using Wave (plus), or other standard data visualization libraries in the Python and R stacks or using Tableau/PowerBi.
Understanding and experience with post production model monitoring concepts and tools like ML Ops (plus) MLFlow etc.
In depth understanding of machine learning algorithms and experience applying Gen AI/ML to solve real work problems, including deployment of end to end ML pipelines
Interest in Generative AI and Large Language Models (Prompt Engineering, Retrieval Augmented Generation, LLMOps, Making custom LLM models, fine tuning etc, Guardrails)
Strong problem-solving skills and the ability to work independently and as part of a team.
Excellent communication and presentation skills, with the ability to convey complex technical concepts to non-technical stakeholders.