Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Lead Data Scientist with 13 years of experience designing and deploying production-grade machine learning and enterprise GenAI systems. Expertise spans PyTorch, TensorFlow, secure RAG architectures, and LLMOps frameworks focused on reliability, monitoring, and continuous improvement. I build scalable data pipelines and low-latency inference services, partnering across product, data, and engineering teams to deliver measurable business impact. I’m passionate about productionizing AI solutions with strong governance, privacy, and engineering rigor.…Lead Data Scientist with 13 years of experience designing and deploying production-grade machine learning and enterprise GenAI systems. Expertise spans PyTorch, TensorFlow, secure RAG architectures, and LLMOps frameworks focused on reliability, monitoring, and continuous improvement. I build scalable data pipelines and low-latency inference services, partnering across product, data, and engineering teams to deliver measurable business impact. I’m passionate about productionizing AI solutions with strong governance, privacy, and engineering rigor.

Kenan Sooklall

AI Engineer, Data Scientist, Lead Sound Engineer, +2





Lead Data Scientist with 13 years of experience designing and deploying production-grade machine learning and enterprise GenAI systems. Expertise spans PyTorch, TensorFlow, secure RAG architectures, and LLMOps frameworks focused on reliability, monitoring, and continuous improvement. I build scalable data pipelines and low-latency inference services, partnering across product, data, and engineering teams to deliver measurable business impact. I’m passionate about productionizing AI solutions with strong governance, privacy, and engineering rigor.…Lead Data Scientist with 13 years of experience designing and deploying production-grade machine learning and enterprise GenAI systems. Expertise spans PyTorch, TensorFlow, secure RAG architectures, and LLMOps frameworks focused on reliability, monitoring, and continuous improvement. I build scalable data pipelines and low-latency inference services, partnering across product, data, and engineering teams to deliver measurable business impact. I’m passionate about productionizing AI solutions with strong governance, privacy, and engineering rigor.

Available to hire

Lead Data Scientist with 13 years of experience designing and deploying production-grade machine learning and enterprise GenAI systems. Expertise spans PyTorch, TensorFlow, secure RAG architectures, and LLMOps frameworks focused on reliability, monitoring, and continuous improvement.

I build scalable data pipelines and low-latency inference services, partnering across product, data, and engineering teams to deliver measurable business impact. I’m passionate about productionizing AI solutions with strong governance, privacy, and engineering rigor.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Lead Data Scientist at LiveRamp

June 1, 2024 - Present

Led design, development, and deployment of enterprise GenAI platforms and agentic AI systems using PyTorch and TensorFlow. Implemented secure RAG architectures with vector databases and retrieval pipelines to deliver accurate context-aware decision support for Fortune 500 clients across multiple data domains. Built LLMOps frameworks including model versioning, A/B testing, feature stores, drift detection, and automated retraining cycles, reducing model degradation incidents by 60%. Established AI governance for model risk assessment, data privacy compliance, and ethical AI guidelines to enable secure enterprise deployment on AWS and GCP. Developed scalable data pipelines and backend APIs with Python, FastAPI, and SQL, supporting 5x growth in processing volume. Containerized model serving with Docker and Kubernetes, implemented CI/CD, and enabled automated monitoring dashboards for anomaly detection, alerts, and low-latency high availability inference.

Senior Data Scientist at McKinsey & Company

September 1, 2019 - June 1, 2024

Developed predictive models and optimization algorithms for global clients using Python, TensorFlow, and scikit-learn, improving operational efficiency by 25% and delivering significant annual cost savings. Owned end-to-end data science projects from problem definition through deployment and integration into client decision systems. Designed and deployed cloud-based ML services on AWS and GCP using Docker containers and serverless functions, reducing infrastructure costs by 30% and supporting elastic workloads across geographies. Built automated reporting and model monitoring pipelines with SQL and Dash, enabling real-time tracking for dozens of production models and reducing manual review effort by 70%. Led technical workshops and code reviews, promoted MLOps best practices, and created reusable internal libraries/tools for data access, feature computation, and model evaluation to accelerate delivery timelines.

Software Engineer at Cognizant

January 1, 2015 - July 1, 2019

Engineered backend systems and RESTful APIs in Python and Java to integrate data services with enterprise applications, reducing transactional errors by 30% in high-traffic production environments. Implemented scalable data processing pipelines using SQL and Apache Spark to process terabytes of structured/unstructured data, reducing data preparation time by 40%. Automated deployment workflows with Docker and Jenkins to support CI/CD for microservices, reducing release cycle times by 50% and improving deployment success rates. Collaborated with data science teams to productionize ML models and integrate them into web applications with response times under 100 ms. Optimized PostgreSQL/MySQL queries and schemas to improve performance by 40% and support high-concurrency traffic with reduced operational overhead.

Associate Data Scientist at Infosys

October 1, 2013 - January 1, 2015

Analyzed large datasets with Python and SQL to deliver business insights for banking and healthcare clients, improving data accessibility and executive decision-making speed. Developed ML models (regression, classification, clustering) for use cases such as churn prediction and credit risk scoring, reaching 85% model accuracy and deploying models with ongoing monitoring via SQL dashboards. Performed data cleaning, feature engineering, and exploratory analysis on structured/unstructured data, improving accuracy by 15% through iterative feature selection and cross-validation. Automated data extraction/transformation workflows, reducing manual processing time by 45% using reusable scripts and scheduled jobs. Prepared model documentation/validation reports and client presentations, and built dashboards using Tableau and matplotlib to support stakeholder understanding.