Sagar Palao — AI Researcher

I’m a Machine Learning Scientist at Netflix, working on high-impact problems across Netflix, currently focusing on Netflix Games. I operate as a full-stack ML practitioner -- building end-to-end systems from data pipelines and modeling to deployment and product integration. My work spans forecasting, audience understanding, and player behavior, powering decisions around game strategy, greenlighting, and long-term engagement. I’ve led the redesign of flagship forecasting models, developed pre-launch audience prediction systems, and built propensity and lifecycle models to better understand and grow the player ecosystem. Previously, I spent four years at Morgan Stanley, where I started as a full-stack software engineer before transitioning into a Machine Learning Engineer role. This foundation continues to shape my approach to ML--combining strong systems thinking with modeling depth. I’ve built production-grade recommendation and search systems (BERT-based), developed ML solutions across cybersecurity and NLP, and architected large-scale batch and real-time data platforms. I also optimized high-frequency trading systems for significant efficiency gains. I hold a Master’s in Computer Science (4.0 GPA) from the University of Massachusetts Amherst, where I researched multimodal LLMs in collaboration with Amazon Alexa AI. I earned my Bachelor’s from the University of Mumbai as a Gold Medalist. I’m particularly interested in building scalable, interpretable ML systems and applying modern AI -- especially LLMs -- to real-world, high-impact problems.

Stackforce AI infers this person is a Machine Learning expert in the Gaming industry with a strong software engineering foundation.

Location: San Francisco, California, United States

Experience: 7 yrs 4 mos

Skills

Machine Learning
Ai Development

Career Highlights

Expert in building end-to-end ML systems.
Led redesign of flagship forecasting models.
Strong background in both ML and software engineering.

Work Experience

Netflix

Senior Machine Learning Scientist (8 mos)

Senior Data Scientist (6 mos)

Data Scientist (2 yrs 2 mos)

Data Science and Engineering Intern (3 mos)

Amazon

Graduate Student Researcher (4 mos)

Morgan Stanley

Machine Learning Engineer (Manager Technology) (1 yr 7 mos)

Machine Learning Engineer (Senior Associate Technology) (1 yr)

Full Stack Engineer (Associate Technology) (1 yr)

Full Stack Engineer (Technical Analyst) (5 mos)

Education

Master of Science - MS at University of Massachusetts Amherst

Bachelor of Engineering - BE at University of Mumbai

Engineering Diploma at Maharashtra State Board of Technical Education (MSBTE)

High School at Holy Cross High School

Sagar Palao

AI Researcher

San Francisco, California, United States7 yrs 4 mos experience

AI ML PractitionerAI Enabled

Key Highlights

Expert in building end-to-end ML systems.
Led redesign of flagship forecasting models.
Strong background in both ML and software engineering.

Stackforce AI infers this person is a Machine Learning expert in the Gaming industry with a strong software engineering foundation.

Contact

Skills

Core Skills

Machine LearningAi Development

Other Skills

Agentic AI DevelopmentForecastingModelingModel DeploymentData AnalysisNatural Language ProcessingPyTorchLarge Language Models (LLM)Reinforcement LearningDeep LearningMachine Learning (Theory and Applied)PythonSoftware DevelopmentWeb ApplicationsAndroid Development

About

Experience

7 yrs 4 mos

Total Experience

4 yrs

Average Tenure

3 yrs 4 mos

Current Experience

Netflix

4 roles

Senior Machine Learning Scientist

Promoted

Sep 2025 – Present · 8 mos · Los Gatos, CA · On-site

Re-designed the flagship DAU forecasting model from the ground up, significantly improving long-term accuracy and coverage on a wider range of titles to drive game performance designation and strategic planning.
Architected and deployed the flagship end-to-end model to predict pre-launch audience size for Netflix Games titles, informing greenlighting and investment decisions.
Built a fully agentic AI analytics assistant, enabling conversational natural-language queries over Netflix games data and autonomously generating/executing code (e.g., k-means clustering, Prophet forecasting) for advanced analysis and visualization.

Agentic AI DevelopmentMachine LearningAI Development

Senior Data Scientist

Mar 2025 – Sep 2025 · 6 mos · Los Gatos, CA · On-site

- Developed a suite of Audience Understanding models, including clustering of profiles and games, autoencoder-based play pattern modeling, and supervised audience size prediction models built on top of Netflix foundation model decoding our audience and shaping the strategic roadmap for the gaming verticals.

Machine Learning

Data Scientist

Jan 2023 – Mar 2025 · 2 yrs 2 mos · Los Gatos, CA · On-site

Designed cornerstone propensity models to predict game adoption and return behavior at profile grain, serving as the backbone for multiple Causal Inference studies, Games Merchandising effort, and Willingness-to-Pay studies across Netflix Games.
Analyzed the interplay between streaming and gaming behavior to quantify impact on overall Netflix engagement and retention.
Conducted deep player journey and dormancy analysis using state-machine frameworks to model lifecycle transitions and re-engagement dynamics.

Machine Learning

Data Science and Engineering Intern

May 2022 – Aug 2022 · 3 mos · Los Gatos, CA · On-site

Built a classification model to classify regular vs. non-regular players, enabling targeted gaming initiatives.
Performed petabyte-scale Player lifecycle analytics with Spark for Netflix Games, covering engagement modeling, churn and re-joiner analysis, gateway game identification, and gaming vs. streaming behavior insights.

Amazon

Graduate Student Researcher

Jan 2022 – May 2022 · 4 mos · Remote

Graduate Student Researcher with the Amazon Alexa AI Team. Conducted a comprehensive analysis of backward compatibility in Multimodal LLMs Pre and Post training updates, developing novel loss functions and parameter biases to mitigate regression across architectural and data updates.

Morgan stanley

4 roles

Machine Learning Engineer (Manager Technology)

Jan 2020 – Aug 2021 · 1 yr 7 mos · Mumbai · On-site

Applied SHAP to interpret and communicate predictions from multiple ML models, enabling transparency and stakeholder trust.
Developed a brand domain infringement detection model using domain features and fine-tuned CharacterBERT (F1: 0.96).
Led teams to build and deploy anomaly detection (F1: 0.88), device classification (Cohen’s Kappa: 0.99), DNS tunneling detection (F1: 0.94), and periodic pattern mining models.
Implemented an algorithm to mine periodic patterns in the network data.
Optimized the streaming resources: 600 GB to 40 GB, 200 cores to 10 cores, and the processing time from 4.7 min to 50 s in Spark streaming.
Conducted ML workshops and trained women in technology who want to resume their careers.

Machine Learning

Machine Learning Engineer (Senior Associate Technology)

Promoted

Jan 2019 – Jan 2020 · 1 yr · Mumbai · On-site

Built the flagship article recommendation engine for Financial Advisors using Factorization Machines (nDCG: 0.7).
Elevated Financial Advisor’s Search experience by deploying a fine-tuned Siamese BERT model for Q&A retrieval and BERT-based topic modeling, increasing user satisfaction from 40% to ~88%.
Designed a GRU-based model to forecast trending articles (F1: 0.87), enabling proactive demand planning.

Machine Learning

Full Stack Engineer (Associate Technology)

Jan 2018 – Jan 2019 · 1 yr · Mumbai · On-site

Architected an end-to-end batch and real-time analytics platform that became the backbone of the firm’s analytics ecosystem.
Optimized the High Frequency Trading platform using C/C++ and CUDA, reducing memory from 600GB to 40GB and compute from 200 to 10 cores with no latency impact.