Satish Kumar

About Satish Kumar

I am actively looking for opportunities in Senior/Staff Data scientist roles. I am a seasoned Data Science & Analytics professional with experience in data science & analytics, statistics, and machine learning. My potent mix of technical skills and analytical acumen enables me to transform data into actionable insights. I\'ve built data pipelines processing 30 Billion rows of data and deployed ML models scoring 132 Million records of data at Pacific Gas and Electric. At Apple, I have deployed priority sorting algorithms and delivered production ready code under tight deadlines and built and deployed powerful Streamlit SKILLS MLOps - Model Deployment with Azure and Streamlit Deep Learning -(CNNs, LSTMs, RNNs, BERT, VAE, GAN, Transformers) Reinforcement Learning -(DQN, MDP, SARSA, Actor-Critic) Big Data -(SQLite, Spark, Scala, AWS Athena, Docker, GCP, Microsoft Azure Databricks) Python, PyTorch, PySpark, SQL, R, Tableau, PowerBI, HTML, CSS, D3.js, JavaScript, Java Data Visualization Statistics NLP A/B Testing, Hypothesis Testing Complex Problem-Solving Cross-Functional collaboration AI & Analytics – LLM prototyping, AI/ML, NLP, Computer Vision, Forecasting, GenAI Product Strategy & Road mapping – B2B/B2C, KPIs, OKRs SaaS Integrations – Azure, HubSpot, D365, Stripe, Auth0 Tools & Platforms: Azure DevOps, JIRA, Figma, Smartsheet, ChatGPT, Hugging Face, Botsonic Cloud & container orchestration -Kubernetes, OpenShift, AWS/Azure/GCP API gateway/service mesh -Istio, Envoy, Kong, Apigee

Experience

1. Senior Data Scientist Machine Learning Engineer

   Pacific Gas and Electric Company

   Dec 2018 — Present · San Ramon, CA, US

   Artificial Intelligence Engineer in Responsible AI (Classical machine learning, deep learning, GNNs and Large Language models)- Governance and Compliance: Part of the Responsible AI Governance (RAIG) program, ensuring ethical AI use and regulatory compliance- Fairness and Accountability: Promotes fairness and inclusion in AI systems, developing and implementing fairness criteria and mitigation strategies- Transparency and Trust: Builds technology to assess and mitigate RAI risks, fostering transparency and accountability in AI development- Regulatory Preparedness: Prepares LinkedIn for upcoming AI regulations, creating infrastructure and processes for compliance- Cross-Functional Collaboration: Collaborates with key stakeholders across various functions to implement RAI principles effectively.