Data Science Practice Lead
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As the Data Science Practice Lead you will be working across multiple technologies developing IoT-centric solutions. This requires an interest in sensors, control systems, cloud ops and everything in between…
We value well-tested, reusable code and expect our engineers and data scientists to be as good of practitioners as they are leaders and teachers.
About This Role
As the Data Science Practice Lead at Very, you will define and deploy the overall strategy for our Data Science practice. Our goal is to build reliable machine learning systems with agility, and you will define the path to help us get there. An ideal candidate will display technical expertise in machine learning and data science, as well as strong leadership and other soft skills such as technical writing and public speaking.
What You’ll Be Working On
Very is first and foremost a software consultancy. We tackle hard problems for clients who need a targeted, senior team to come in and provide specific solutions. There is a never-ending supply of variety to the types of projects we work on. However, it is critical to note that almost all of these projects are production systems. As such, the only consistent research component of this position will revolve around establishing a pattern of delivery that allows the team to implement full-scale applications leveraging machine learning in a fast, predictable manner.
You’ll spend 80% of your time working on a product or platform for one of our clients, and the other 20% of your time will be spent improving Very’s Data Science Practice. This will involve:
- Working with our data scientists to define (and continuously refine) our delivery process for data science applications.
- Working with our marketing team to generate high-quality content (blog posts, conference presentations).
- Working with our sales team to close deals and build meaningful proposals for potential clients.
Our Current Tooling
Our data science contracts typically involve a greenfield API or greenfield product from the ground up. In the context of the data science and machine learning pipelines, we typically leverage:
- Jupyter notebooks for prototyping
- The standard SciPy Stack (Numpy, SciPy, Pandas, Scikit-Learn, Matplotlib)
- AWS Lambda via the Serverless Framework
- AWS Sagemaker
- AWS Batch
On our full-service builds, we often reach for the following tools:
- Elixir, Phoenix, and Nerves
- AWS IoT (MQTT)
- AWS Lambda and the Serverless Framework
- AWS SageMaker
- Embedded C, C++ (Nordic, Espressif, ST)
- Mobile: iOS, Android, React Native
You don’t need to be an expert in these tools, but familiarity is a plus. Our build teams operate with a very high degree of collaboration, so you will definitely have run-ins with these stacks throughout your time here.
How You’ll Be Compensated
We believe in a transparent, fair compensation structure and have developed our own open salary formula. Depending on your skill and experience, you can expect your base compensation to be somewhere between $140,000 and $175,000 upon joining the company. We also offer performance bonuses, a generous maternity/paternity leave policy, 401K matching, and numerous other employee benefits including reimbursement for home office equipment and gym memberships.
This is a full-time employment opportunity for a single individual. We’re not looking for contractors, part-time individuals, or agencies of any kind. Applicants must be located in the continental United States. Thanks!