Job description
As our Data Scientist you will design and train performant models from scratch and create a powerful analytics engine fueled by cutting-edge technology and generative AI. You will be responsible for building, validating, and deploying machine learning models that drive our product's analytics engine. Operating in an Agile setting, you will be part of a cross functional team, collaborating closely with other Data Scientists, Data Engineers, Software Engineers, Product Owners and UX /UI Designers, to innovate and drive our product to new heights.
If you are passionate about building impactful digital products and want to be part of a team committed to making the world a better place, Element is the perfect opportunity for you. Join us and make your mark on the world, one innovative product at a time.
- You will build and deploy advanced machine learning models & design robust analytics engines for high-end digital products
- You will create repeatable, interpretable, dynamic and scalable models that are seamlessly incorporated into analytic data products
- You will document code, results, and details of the approaches in a systematic way to promote knowledge sharing and code re-use
- You will collaborate with other data scientists to ideate and solve complex issues pertinent to data ingestion /curation, modality integration, model performance, model generalization
- You know how to effectively communicate the analytics approach and how it will meet and address objectives to business partners (both technical and non-technical audiences)
- You love to stay up-to-date with latest industry trends and advances in data science, machine learning and generative AI
- You will advocate and educate on the value of data driven decision making focusing on the “how and why” of solving problems
- Lead analytic approaches, integrating work into applications and tools with data engineers, business leads, analysts and developers
- You have a Bachelor’s in Data Science, Computer Science, Engineering, Statistics and/or 5+ years experience required OR MS or PhD in quantitative discipline and demonstrated Data Science skill set, plus 2+ years work experience
- You have extensive knowledge and expertise in building, testing, and deploying complex machine learning models that demonstrate high accuracy and robustness
- You have strong proficiency in Python, PostgreSQL and Azure Databricks
- You have experience with supporting deployment, monitoring, maintenance and enhancement of models
- You have epertise in using and implementing cutting-edge machine learning algorithms, frameworks, and libraries, such as PyTorch, Keras, Tensorflow to solve clustering, classification, regression, anomaly detection, simulation and optimization problems on large scale data sets
- Experience with NLP tasks preferred
- Experience with Big Data technologies desired — Hadoop, Spark, H20.ai, Cloud AI platforms, containerization
When failure in use is not an option, we help customers make certain that their products, materials, processes and services are safe, compliant and fit for purpose. From early R&D, through complex regulatory approvals and into production, our global laboratory network of scientists, engineers, and technologists support customers to achieve assurance over product quality, sustainable outcomes, and market access.
While we are proud of our global reach, working at Element feels like being part of a smaller company. We empower you to take charge of your career, and reward excellence and integrity with growth and development.
Industries across the world depend on our care, attention to detail and the absolute accuracy of our work. The role we have to play in creating a safer world is much bigger than our organization.
All suitably qualified candidates will receive consideration for employment on the basis of objective work related criteria and without regard for the following: age, disability, ethnic origin, gender, marital status, race, religion, responsibility of dependents, sexual orientation, or gender identity or other characteristics in accordance with the applicable governing laws or other characteristics in accordance with the applicable governing laws.