Narvar is growing! We are hiring a Senior Machine Learning Engineer to build new products and improve all aspects of the Narvar platform. Data is at the core of our competitive advantage so the work you do has a large impact across the company, our business partners, and the lives of our end users.
As a “Full Stack” Data Scientist, you will write production code that solves business problems and work in a fast paced high-achievement team setting. This is an excellent opportunity to help expand the data science practice and work with talented developers and scientists.
- Build machine learning pipelines for use cases spanning e-commerce, consumer trends, markets, logistics, and new products
- Work on real world consumer data for natural language processing, image classification, time series analysis, outlier detection, user modeling, etc
- Work with large unstructured data
- Be at the intersection of mathematics, machine learning, business, and computer science and impact millions of users through your work
- Multiply the effect of your data science and data team members by building frameworks, tools, and methodologies that the entire team use
- Provide thought leadership to a team through high quality write-ups, reviews, and a strong vision grounded in practical experience and a wider industry view
What we’re looking for
- Data science skills. Experience with machine learning or optimization modeling and know how to create visualizations in order to tell a story
- Data Engineering skills and large data experience. You should have dealt with large amounts of data (TB) in a production setting, built world class data pipelines using cutting edge tools (eg: Spark, Hadoop etc)
- 5+ years of hands-on experience shipping models to production, working on a variety of problem spaces (eg: user modeling, spam classification, prediction, clustering etc)
- Ph.D. or MS in Computer Science, Statistics, Math, Science (physical or social), Engineering or similar quantitative and computation field plus 5+ years of industry experience
- Strong software engineering and coding skills with the ability to write production quality code
- Strong understanding of probability & statistics, and/or machine learning, and algorithms
- Fluency in Python, Pandas, Spark, numpy, and machine learning packages
- Experience implementing applications on Amazon AWS or Google Cloud Platform
- Experience with SQL and NoSQL databases
- Experience working with Linux, shell scripting
- Experience with deep generative models a plus.
- Tag your application with your solution to an active / recently concluded Kaggle competition, to get ahead of the list :)
Experience leading a team of data and data scientists; Strong ability to multiply the effect of the team and the team members; excellent written communication; self-starter that can balance sophistication with practicality
We're on a mission to simplify the everyday lives of consumers. We believe post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. If you've ever bought something online, there's a good chance you've used our platform!
From the hottest new direct-to-consumer companies to retail’s most renowned brands, Narvar works with Patagonia, GameStop, Neiman Marcus, Sonos, Nike and 850+ other brands. With offices in San Francisco, London, Paris, and Bangalore, we've served over 125 million consumers worldwide across 8 billion interactions, 38 countries, and 55 languages.
Pioneering the post-purchase movement means navigating into the unknown. Our team thrives on this sense of adventure while nurturing a mindset of innovation. We're a home for big hearts and we leave our egos at the door. We work hard but we always make time to celebrate professional wins, baby showers, birthday parties, and everything in between.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.