Machine Learning Scientist
Development | Austin, TX | Full Time
Revionics has an immediate opening in our Science Development Team for a Machine Learning Scientist who will lead R & D efforts to deliver data-driven solutions to business problems. This individual must be knowledgeable in advanced mathematics to recognize patterns, identify opportunities, and make valuable discoveries leading to prototype development, model trouble shooting and product improvement. The successful candidate must be creative and willing to develop a thorough understanding of the science and business rationale behind Revionics’ products, services, and methodologies.
Revionicsdelivers the industry’s most powerful End-to-End Merchandise Optimization solution, enabling retailers of all sizes to execute a fact-based Omni-channel merchandising strategy utilizing the most comprehensive set of shopper demand signals to enhance financial performance with improved customer satisfaction. Revionics’ solutions leverage advanced predictive analytics and demand-based science to ensure retailers have the right product, price, promotion, placement and space allocation for optimal results across all touch points in the Omni-channel shopping episode – online, in-store, social and mobile. Offered on a scalable, high-performance Cloud-based SaaS platform, these solutions future-proof retailers from Big Data/Fast Data challenges, while providing speed-to-ROI. Over 33,000 retail locations and $140+B in annual revenue across grocery, drug, building materials, convenience, general merchandise, discount, sporting goods stores, and eCommerce sites optimize with Revionics’ solutions. Revionics has been recognized as a 2012 Deloitte Technology Fast 500™, Red Herring Top 100 Global, Red Herring Top 100 Americas, and JMP Securities’ Hot 100 Software Company. For more information, please visit www.revionics.com.
Who you are:
The Machine Learning Scientist is responsible for designing and supporting Revionics’ analytical pricing software. You will be responsible for developing an expert-level understanding of the core scientific capabilities of Revionics’ solutions, configuration of the system to maximize value, and delivery of analytical services to Revionics’ customers. The Machine Learning Scientist must also be able to explain advanced concepts to business users and customer IT personnel.
- Apply knowledge of statistics, machine learning, programming, data modeling, simulation and advanced math to recognize patterns, identify opportunities, pose business questions and make valuable discoveries leading to prototyping development and product improvement
- Use a flexible, analytical approach to design, develop and evaluate predictive models and advanced algorithms that lead to optimal value extraction
- Generate and test hypotheses and analyze and interpret the results of product environment
- 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 guidelines for large-scale implementation
- Knowledge transfer throughout the organization, internal presentations, and white papers establishing thought leadership and capability excellence in the Revionics platform
- Coordinate and conduct testing and analysis of solutions across customer data sets
What You Have/Can Do as a Minimum:
- Advanced degree (Masters or Ph.D.) in Mathematics, Statistics, Engineering, CS, or Physical Sciences
- Strong background in statistical regression and modeling techniques (least squares, maximum likelihood, Bayesian estimation)
- Strong Python skills and familiarity with tools such as scikit-learn, pandas, etc.
- SQL and relational databases skills (MS SQL Server, Oracle, etc.)
- Knowledgeable of a broad range of mathematical techniques, tools, and modeling frameworks and able to assess their relative merits and applicability to specific problems
- Hands-on data analysis experience and the ability to produce data visualizations to present complex data graphically (distributions, scatter plots, sensitivity analyses)
- Ability to express real-world processes in the languages of mathematics and probability
- Excellent communications skills and a team player
What You Can Do to Stand Out:
- Proficiency with Cloud providers like AWS, GCP, Azure, etc.
- Proficiency with non-relational scalable data stores (Spark, Hadoop, MongoDB, etc.)
- Proficiency in an object-oriented language such as C++, Java, or C#
- Domain expertise in price optimization, demand forecasting, or inventory optimization
- Proficiency with data visualization libraries (Bokeh, Matplotlib, d3.js)
- Monte Carlo methods or other simulation techniques (Stan, PyMC, BUGS)
- Deep learning and autodifferentiation libraries (Tensorflow, Torch, Theano)
- Other optimization and machine learning techniques (linear/nonlinear programming, genetic algorithms, support vector machines, ensembling, etc.)
- Hierarchical and non-hierarchical clustering techniques (K-means, agglomerative clustering, divisive clustering, graph theory, QT clustering)
- 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
Who We Are:
Predictive. Prescriptive. Profitable Retailing.
We provide SaaS-based pricing, promotion, markdown and space solutions. Retailers in all segments across the world adopt our self-funding model to improve top-line sales, demand, and margin. Our customers gain that competitive edge and improve their value proposition while outmaneuvering competitor price aggressiveness.
During the days of first-generation price optimization solutions, at a time where science in retail was viewed as voodoo, our founder Jeff Smith nurtured the concept that there could be a better way. He went on to form Revionics around that new-generation vision, and to this day we remain committed to his goal: To help retail businesses and everyday users solve complex pricing challenges leveraging the latest machine learning science with a completely transparent process, usable in an intuitive way that fits into retailers’ normal business flows.
Our company success is based on our 4 foundational pillars:
- A SaaS-based architecture for fast ROI
- Productized, transparent science
- Machine Learning algorithms that continue to evolve with changing market conditions and shopper behaviors for built-in future proofing
- A supportive culture focusing on both our people and customers’ well-being.
Our Core Values:
- Integrity: Be honest, dependable and complete
- Transparency: Anticipate questions and give clear, usable answers.
- Continuous Improvement: Be relentless about improvement – for ourselves and our customers
- Curiosity: Shine lights in dark corners; seek to ensure we know what we don’t know
- Accountability: Own the problem and the solution
- Dedication: Don’t stop until the numbers are right and systems are up
- Humility: Put the spotlight on our customers, not ourselves