About the role:
Elemental Machines is looking for a Data Scientist to be a key member of Elemental’s fast growing Data Science and Analytics team. Designing and documenting your model choices, contributing to technical leadership, and working closely with other engineering teams to deliver industry leading analytics built on top of a world class IoT platform.
Your primary focus will be on building and improving machine learning models for customer facing products. Novel IoT analytics are mission critical to our business as we grow and continue to expand our product offerings. Each new model offers a great opportunity to implement new solutions and integrate third-party services to solve business and technical challenges.
A successful candidate must be creative, willing to innovate, and possess interest and aptitude to succeed in a startup environment. You must be able to work independently, understand the needs of the business, proactively reprioritize as needs change and strive for personal growth in line with company goals. You are comfortable with learning about and contributing to all things machine learning.
A bachelor’s degree is required, with a strong working knowledge of Python and 3+ years experience in time series analysis and machine learning.
This position offers an excellent opportunity for professional growth in areas of near real-time data processing and analytics of IoT data streams. You will be instrumental in developing models that directly impact the success of our customers and therefore the business.
- Your solutions will have immediate impact on the efficiency of the scientific process
- Develop advanced machine learning models and algorithms on time series sensor data for product features
- Research, prototype, deploy and own time series algorithms and predictive models, such as classification, forecasting, anomaly detection, event detection and signal processing
- Identify and build new datasets to enhance the models and product features
- Communicate results to technical and non-technical stakeholders
- 3+ years experience as a data scientist or in a similar role
- Knowledge of programming languages (e.g. Python (preferred) or R)
- Experience with time series analysis (anomaly detection, event detection, forecasting and/or classification)
- Experience training and tuning machine learning classification models for better precision or recall
- Hands-on experience with relational and time series databases (SQL)
- Experience with version control software (e.g. GitHub or bitbucket)
- Great numerical and analytical skills
- Graduate degree, or a bachelor's degree with 3-5 years professional experience, in Mathematics, Statistics, Computer Science, Electrical Engineering, Physics, or a related field
- Knowledge of the GCP cloud environment and tools a plus
Today’s research, clinical, and quality control labs are expected to discover and produce at a pace once considered unimaginable. Now, to lead is to be led by data. The Elemental Machines platform elevates LabOps teams to lead data-driven discovery, development, and delivery by simplifying the connection between physical and digital worlds. The result is a lab united by a universal cloud-connected dashboard and operations, informed by monitoring of every asset and environment, both in real-time and over time. Why? The standards established by operations determine the pace of output and discovery organization-wide.
Elemental Machines is a startup company based in Cambridge, MA, and we love it here! Being right next to some of the most innovative companies in the IoT and BioTech industries gives us drive and passion to pursue our own big goals, while enabling other companies to pursue theirs.
We are committed to delivering on our promise to our customers: accelerate how they get science done. This commitment touches every level of our culture, because we are excited every day to stretch our creativity in balancing the needs of our customers with the resources at our disposal, and to thoughtfully interact with our peers to keep our mission on track.
Excellence in LabOps is a connection away.
What do we do?
We elevate LabOps teams to lead data-driven discovery, development, and delivery.
How do we do it?
We simplify the connection between the physical and digital worlds.
Why do we do it?
Optimizing the operations of research, clinical, and quality control labs has an exponential impact.