Data Scientist - Optimization

Tiger Analytics
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Job Description

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

As a Data Scientist, you will apply strong expertise in AI through the use of machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engines and services. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Key Responsibilities

  • Design prescriptive type analytical models using python docplex, commercial languages/systems/solvers (OPL, CPLEX, IBM DOC), ad-hoc heuristics or metaheuristics
  • Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools.
  • Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
  • Research & design algorithms to solve problems across the organization touching the various parts of the business
  • Implement & coordinate with the development team to translate the model into production code
  • Going beyond established analytical thinking and problem-solving by applying creativity to unconventional concepts and out-of-the-box solutions
  • Lead OR approaches; integrate solutions collaboratively into applications and tools with data engineers, business leads, analysts and developers.
  • Create repeatable, interpretable, dynamic and scalable ML/OR models that are seamlessly incorporated into analytic data products.
  • Stay connected with external sources of ideas through conferences and community engagements.

Requirements

  • PhD in Mathematics, Statistics, Industrial Engineering, Operations Research or a related field with statistics courses/publications/proven expertise or Master's degree plus 2 years of industry experience
  • Experience in Supply Chain or Retail.
  • Strong mathematical background (linear algebra, calculus, statistics, optimization).
  • Strong coding skills including optimization algorithms
  • Deep understanding of optimization (constrained, convex and non-convex optimization problems, IP and LP, QP, MILP, MINLP, problems and solvers)
  • Being able to translate real world situations into mathematical constraints and objective functions
  • Ability to drive a project and work both independently and in a team
  • Fluent in optimization modules in Python or R. Experience with Gurobi is preferred.
  • Exceptional communication and collaboration skills to understand business partner needs and deliver solution

Company Info.

Tiger Analytics

Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 500 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.

  • Industry
    Consulting,Artificial intelligence,Computer software
  • No. of Employees
    700
  • Location
    Santa Clara, CA, USA
  • Website
  • Jobs Posted

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Tiger Analytics is currently hiring Data Scientist Jobs in Dallas, TX, USA with average base salary of $120,000 - $190,000 / Year.

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