Machine Learning Engineer, Tech Lead
The Business Intelligence market is undergoing a revolution rooted in the idea that not everyone will be a data expert; however, we believe people should be empowered to make decisions using data. Data storytelling is critical to addressing this problem, and Narrative Science is at the cutting edge of this movement. To support this mission, our engineers are at the forefront—the most successful engineers at Narrative Science balance drive, resilience, humility, and creativity.
As the Machine Learning Tech Lead, your core is engineering but you have a passion for analytics, statistics, or machine learning. We expect that all engineers work across our stack to some degree, but most engineers at Narrative Science have a primary focus. Ideally, you have end-to-end experience developing, deploying, and integrating ML solutions into SaaS systems.
This role's primary focus is on Data Science and Machine Learning Modeling techniques. You will be expected to deliver moderate and advanced models that will be used in production to support multiple different customer goals. Being knowledgeable of MLOPs best practices and working with engineers to build out architecture that takes models through development and training to production deployment will be a focus.
As a Tech Lead you will be working with our product and development teams to improve the analysis and content in our stories. In this role, you will have a solid mix of greenfield projects and platform improvements, along with plenty of opportunities to prototype and build lasting features and enhancements. You will be expected to grow and mentor junior developers as they work with you to deliver enterprise-grade solutions. You will work directly with Engineering leaders as you drive out designs and break down ambiguous customer problems into actionable tickets for the team to deliver.
- 5+ years of professional software development experience or statistical model development and research
- Proficiency in Python or another language
- Demonstrated ability to write production code that is well commented
- Previous experience mentoring and coaching engineers
- A drive to solve customer problems
- A product-first and customer-focused mindset
- A desire to grow as a Data Science Leader
- Experience with Data Science Stack such as Sklearn, TensorFlow, Keras, Prophet, etc
- Experience building and deploying ML solutions with statistical languages such as Python or R
- Proven capability for critical thinking, problem-solving, and the patience to see challenging problems through to the end with internal and external stakeholders
- Strong written and verbal communication skills; the ability to concisely speak to business owners, end-users, and engineers
- Experience with Unix / Linux systems (e.g. Ubuntu, CentOS, RedHat, etc.)
- Familiarity with cloud computing, particularly AWS
Engineering Team @ Narrative Science
The Engineering team at Narrative Science specializes in delivering high-value cloud-based solutions to customers. Specifically, we are responsible for building and innovating on an insightful and personalized data storytelling platform. We work a lot with data pipelines, analytics, natural language generation (NLG), continuous deployment tooling, and cloud architecture.
Culture @ Narrative Science
Narrative Science believes that data storytelling can empower everyone to understand and take action from their data. Our mission is to build software that leverages artificial intelligence to automatically turn data into easy-to-understand reports, transform statistics into stories, and converts numbers into knowledge.
Our core virtues (mission-driven, impactful, team-first, innovative) are ingrained in everything we do, from how we develop our technology to how we interact with customers to how we hire people. Since 2020, we have embraced a remote-first approach and our team has the option to work from home or safely from our Chicago office. This flexibility will remain after COVID.
At Narrative Science, we embrace the diverse backgrounds, experiences, and perspectives of our future employees, colleagues, customers, partners, and other stakeholders. We provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, genetic information, or any other characteristic protected by law.