Data Scientist/Python/MLops- 1 Sep (Tues)--6+ years - Video Interview

Designed, developed, and deployed Machine Learning and Deep Learning models for industrial challenges involving large-scale numerical, categorical, and time series data, as well as images and videos.

Extensive experience in Python programming for statistical analysis, custom algorithm development, and business insight generation using ML/DL models.

Proficient in optimization techniques including Genetic Algorithms, Linear and Quadratic Programming, and others

Skilled in comprehensive data workflows: collection, exploratory analytics, data cleansing, feature selection, and model validation.

Experience in deploying AI/ML solutions using MLOPs on cloud platforms such as Azure and AWS.

Solid knowledge of NLP, Large Language Models, Retrieval-Augmented Generation, and ML algorithms such as Decision Trees, Clustering, Support Vector Machines, Artificial Neural Networks, LSTM, CNN, and YOLO, with awareness of their practical advantages and limitations.

Motivated by continuous learning and mastery of emerging technologies and methodologies in the fields of artificial intelligence and machine learning.

Lead end to end design, development, and deployment of ML/DL solutions for complex business and industrial problems involving structured, time series, and unstructured data.

Define modeling strategies and technical approaches, selecting appropriate ML, DL, Computer Vision, Optimization, and LLM techniques based on problem constraints and business impact.

Oversee data science lifecycle governance, including data acquisition, exploratory analysis, feature engineering, model validation, and performance monitoring.

Drive production grade deployment through MLOps, ensuring scalable, secure, and reliable model operations on cloud platforms such as Azure and AWS.

Lead development of advanced analytics and optimization solutions, translating business objectives into prescriptive and decision support models.

Provide technical leadership and mentoring to data science teams, establishing best practices in Python engineering, experimentation, and model reliability.

Collaborate with stakeholders to translate business needs into AI solutions, clearly communicating assumptions, risks, outcomes, and measurable value.

Continuously evaluate emerging AI/ML and LLM technologies, driving innovation, POCs, and capability building aligned with organizational strategy.