Principal Data Scientist

Company:
Location: Austin, TX

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Revionics, an Aptos company has an immediate opening for a Senior / Principal Data Scientist to join our Austin or Bangalore team. A successful candidate will have an ability to lead complex data science and big data-centric projects. He/she will have a strong working knowledge of the ML/AI development lifecycle and will be proficient with modern tools and techniques for building, deploying and monitoring at scale.

Aptos' market-leading platform drives the world's largest retailers' product pricing, promotion and merchandising decisions across online and brick-n-mortar operations. Over 33,000 retail locations and $200+B in annual revenue across grocery, drug, convenience, general merchandise, discount, sporting goods stores, fashion and eCommerce sites optimize with Aptos' solutions.

About the Role:

The Senior / Principal Data Scientist is responsible for designing, developing and evolving Aptos' demand modeling, forecasting and optimization software. You will need to be creative in the combined application of Bayesian methods, general machine learning and reinforcement learning (among other techniques) to maximize financial and customer-focused objectives of retailers in a data-rich environment.

All of the following will be part of your responsibilities:

  • Apply knowledge of statistics, machine learning, programming, data modeling, simulation and advanced mathematics
  • Use a flexible, analytical approach to design, develop and evaluate predictive models and advanced algorithms that lead to optimal value generation
  • Create and implement mathematical models and scientific algorithms including the development of robust, rapid, and efficient numerical algorithms
  • Execution of proof-of-concept pilots and implementation support
  • Work with product engineers to translate prototypes into new products, services and features
  • Provide data science thought leadership throughout the organization, giving internal presentations, and review white papers with colleagues
  • Generate and test hypotheses on-demand as well as in systematic algorithmically-driven A/B and multivariate test systems

Qualifications:

  • Knowledgeable of a broad range of mathematical & statistical techniques, tools, and modeling frameworks and able to assess their relative merits and applicability to specific problems
  • PhD in Computer Science, Math, Physics, Engineering, Statistics or other technical field
  • Experience manipulating large data sets through Python or SQL
  • Development experience in one of the following: Python, Scala, Java, C++
  • Strong algorithmic problem-solving skills
  • 5+ years of experience working with large data sets
  • Experimentation design or A/B testing experience is preferred
  • Strong background in statistical regression and modeling techniques (Bayesian estimation, least squares, maximum likelihood)
  • Proficiency with Cloud providers like AWS, GCP, Azure, etc.
  • Hands-on data analysis experience and the ability to produce data visualizations
  • Ability to express real-world processes in the languages of mathematics and probability
  • Superior verbal, visual and written communication skills to educate and work with cross-functional teams on controlled experiments
  • Demonstrated ability to drive projects
  • A willingness to learn, share, and improve

What You Can Do to Stand Out:

  • Experience working with Hadoop, Pig/Hive, Spark, MapReduce, or Kafka
  • Domain expertise in price optimization, demand forecasting, or inventory optimization
  • Proficiency with data visualization libraries (e.g. Bokeh, Matplotlib, d3.js)
  • Monte Carlo methods or other simulation techniques (Stan, PyMC, BUGS)
  • Deep learning and auto-differentiation libraries (Tensorflow, Torch)
  • Other optimization and machine learning techniques (linear/nonlinear programming, genetic algorithms, support vector machines, ensembling, etc.)
  • Factor analysis, sensitivity analysis, spectral analysis, eigen systems analysis, Principal Components Analysis (PCA)
  • Econometrics, Decision theory, discrete choice models, propensity modeling
  • Reinforcement learning, active learning, dynamic systems

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