I lead the Streaming Science & Algorithms group at Netflix and we work on fascinating predictive modeling and experimentation problems to optimize the streaming user experience. Unique combination of technology, entertainment, and a culture that enables fast-paced innovation. I'm looking for awesome data scientists to join my team at Netflix (see descriptions/links further below). Please ping me if you're interested in working on big data + algorithms that will impact millions of our members around the world! Talks/Interviews: - Keynote at Big Data Innovation 2014, Boston. http://bit.ly/1nZfO5z - Center for Data Innovation, Sep. 2015: http://bit.ly/datainnovinterview

Director, Streaming Science & Algorithms @ My team works at the intersection of big data, analytics & algorithms, and streaming. We work on interesting problems that have a direct impact on the streaming experience of millions of Netflix members around the globe. Problems we tackle are diverse and range from optimizing the streaming quality of experience (QoE) to working with the digital supply chain that goes all the way to the studios in Hollywood. We're looking for sharp algorithmic minds to join us at Netflix as we revolutionize entertainment science! Check out bit.ly/netflixdatascience for more information on some of our work. I'm currently hiring for two roles, please feel free to ping me if you might be a good fit: * Predictive Modeling/Machine Learning: bit.ly/streamingscience * Experimentation & Modeling: bit.ly/streamingexpmodeling From March 2014 to Present (1 year 8 months) Advisor @ From 2014 to Present (1 year) CTO @ CTO: April 2011 - March 2014 Director, Research & Development: February 2009 - April 2011 Lightning Bolt Solutions is a bootstrapped profitable startup that develops web-based software for physician scheduling using advanced Artificial Intelligence and Operations Research techniques. - Launched Lightning Bolt's new NSight physician scheduling software, a web-based SaaS solution for medical groups powered by a state-of-the-art scheduling engine. - Developed mathematical optimization models and algorithms at the heart of the product. Work selected as semi-finalist for the prestigious 2014 Franz Edelman award for operations research, management science, and advanced analytics (awarded by INFORMS). - Led the design, development, and launch of Lightning Bolt's iOS mobile app for physicians. - Added AWS to the hosting architecture to improve the overall user experience. - Responsible for product vision, management, and development. - Manage, mentor and grow engineering team consisting of Ruby on Rails, iOS, and .NET engineers. - Responsible for operations and hosting infrastructure (AWS + co-location). - Overhauled the development process and infrastructure, introducing Agile development with Scrum and Test Driven Development (TDD/BDD). - Led the rebranding of Lightning Bolt in 2011; worked with design agency to determine and implement branding strategy that included a new logo and re-design of the company website. From February 2009 to March 2014 (5 years 2 months) Sr. Operations Research Engineer & Project Manager @ - Implemented Intel's first real-time production scheduling solution for the complex and capital intensive photolithography area in the fab. - Developed and implemented real-time production scheduling solutions for the complex and highly time critical Gate and EPI processing loops to reduce wafer scrap. - Research on equipment preventive maintenance planning and scheduling methods; developed an optimization and heuristic based system for improving maintenance planning at Intel fabs. - Worked with senior management to drive adoption of new methods and processes in Intel's manufacturing facilities to reduce cycle time and increase throughput. - Managed project teams consisting of modelers, software engineers, industrial engineers, process engineers, and operations managers. - Negotiations and technical decisions on vendor products for fab scheduling and dispatching. - Pathfinding effort with Intel Digital Health Group to develop a process oriented approach to improve healthcare delivery. - Collaborated with academia on research related to semiconductor manufacturing operations management; guided academic research funded by NSF and Semiconductor Research Council. - Several publications and conference presentations; 2 US patents filed. From April 2004 to February 2009 (4 years 11 months) Visiting Researcher @ - Worked on Advanced Process Control for wafer fabrication machines using multivariate statistical techniques (part of a National Science Foundation funded research project). - Design of Experiments, Statistical Modeling of manufacturing systems using parametric and non-parametric approaches. Primary objective was to create a profile of cycle time through the fab. - Analysis of an in-house optimization engine for fab planning and scheduling to determine strengths and shortcomings. From January 2003 to May 2003 (5 months) Graduate Research Intern @ - Resident-entity based simulation modeling of the wet etch process area to analyze the effects of dispatching policies, batching decisions, and tool dedication policies. - Developed a method to set targets for the fab to measure fab performance in the absence of historical data, implemented the method using advanced real-time monitoring tools integrated with the execution system. - Developed a simulation model of an ADI testing tool in the lithography area to analyze the effect of real-time changes in sampling rates based on queue sizes. - Implemented rules used to make real-time dispatching decisions in the 300mm fab. - Investigated optimal policies for lot release into the fab. - Developed a linear programming based optimization model to plan the purchase of machines for IBM's 300mm wafer fab. - Built a discrete event simulation model of the fab to predict tool requirements and fab performance for IBM's planned state-of-the-art 300 mm wafer fab. - 2 conference publications resulted from some of the work done at IBM. From June 2000 to August 2002 (2 years 3 months) Researcher, Competitive Semiconductor Manufacturing Program @ The CSM program benchmarked fabs across the world and provided recommendations on best practices based on real data from participating semiconductor manufacturers. Responsibilities included providing comparative data analyses based on fab data from CSM participants. Member of CSM site visit team to AMD's Fab 25 in Austin, TX (2001). From 1998 to 2001 (3 years)

Ph.D., Industrial Engineering & Operations Research @ Pennsylvania State University From 2001 to 2004 M.S., Industrial Engineering & Operations Research @ University of California, Berkeley From 1998 to 1999 B. Tech, Mechanical Engineering @ Indian Institute of Technology, Madras From 1994 to 1998 Nirmal Govind is skilled in: Operations Research, Product Management, SaaS, Algorithms, Agile Methodologies, Web Applications, Analytics, Statistical Modeling, SQL, Scrum, Simulations, Healthcare, Data Mining, Optimization, Semiconductors, Start-ups, Management, Cloud Computing, Enterprise Software, Decision Sciences, Big Data, Data Science, Data Analysis, Statistics, Hive, Machine Learning, Python, Mathematical Modeling, Mathematical..., Business Intelligence, Mobile Applications, Operations Management, Strategy, Project Management, Test Driven Development, Amazon Web Services..., Ruby, Ruby on Rails, Git, PostgreSQL, XSLT, XPath, CPLEX, Xpress Optimizer, R, Matlab, Mosel modeling language, OPL modeling language, NoSQL