Internal Application: Machine Learning Engineer
Development | Austin, TX | Full Time
Revionics has an immediate opening in our Core Data Science Team for a Machine Learning Engineer who will lead development efforts to bring new machine learning algorithms to our production systems. This cross-disciplinary position will work closely with our Data Science team during the development of new models and will help bring those models to production by working with our Software Engineering and Architecture teams to build a scalable and reliable machine learning system. 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.
Revionics delivers 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 Engineer is responsible for designing and supporting machine learning systems within the Revionics’ analytical pricing software. You will be responsible for developing an expert-level understanding of the core scientific capabilities of Revionics’ solutions and building production grade machine learning systems to augment these solutions. The Machine Learning Engineer must also be able to explain advanced machine learning concepts to multiple teams across the business.
- Apply knowledge of machine learning, programming, and data modeling to build productions systems that will train, test, and predict
- 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
- Work with machine learning scientists 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 Computer Science, Engineering, Mathematics, or equivalent experience
- Experience building machine learning systems in a production environment
- Scientific Python development skills and familiarity with tools such as scikit-learn, pandas, etc.
- Experience with OOP and SOLID principles
- SQL and relational database skills (MS SQL Server, Oracle, etc.)
- Proficiency with Cloud providers like AWS, GCP, Azure, etc.
- Software testing and release cycle experience
- Understanding of query tuning and code optimization concepts
- Knowledgeable of a broad range of mathematical techniques, tools, and modeling frameworks and able to assess their relative merits and applicability to specific problems
- Ability to express real-world processes in the languages of mathematics and probability
- Enthusiastic and knowledgeable participant in the scientific software community
- Excellent communications skills and a team player
What You Can Do to Stand Out:
- 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.)
- Hands-on data analysis experience and the ability to produce data visualizations to present complex data graphically (distributions, scatter plots, sensitivity analyses)
- Background in statistical regression and modeling techniques (least squares, maximum likelihood, Bayesian estimation)
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