Sr. Lead Data Scientist / Principal Data Scientist

Overview

Remote
Depends on Experience
Contract - Independent
Contract - W2

Skills

python
PyTorch

Job Details

Role : Sr. Lead Data Scientist / Principal Data Scientist

Location : Remote

Duration : 12 months

Your responsibilities :

Exploring data, building models, and evaluating solution performance to resolve core business problems
Explaining, refining, and collaborating with stakeholders through the journey of model building
Keeping up with your domain's state of the art & developing familiarity with emerging modelling and data engineering methodologies
Advocating application of best practices in modelling, code hygiene and data engineering
Leading the development of proprietary statistical techniques, algorithms or analytical tools on projects and asset development
Working with Partners and Principals to shape proposals that leverage our data science and engineering capabilities

  • Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or other quantitative and computational science
    Compelling track record of designing and deploying large-scale technical solutions, which deliver tangible, ongoing value including:
    Building and deploying robust, complex production systems that implement modern data science methods at scale, including supervised learning (regression and classification with linear and non-linear methods) and unsupervised learning (clustering, matrix factorization methods, outlier detection, etc.)
    Leveraging cloud-based infrastructure-as-code (CloudFormation, Bicep, Terraform, etc.) to minimize deployment toil and enabling solutions to be deployed across environments quickly and repeatably
    Demonstrating comfort and poise in environments where large projects are time-boxed, and therefore consequential design decisions may need to be made and acted upon rapidly
    Demonstrated fluency in modern programming languages for data science (i.e. at least Python, other expertise welcome), covering the full ML lifecycle (e.g. data storage, feature engineering, model persistence, model inference, and observability) using open-source libraries, including:
    Knowledge of one or more machine learning frameworks, including but not limited to: Scikit-Learn, TensorFlow, PyTorch, MxNet, ONNX, etc.
    Familiarity with the architecture, performance characteristics and limitations of modern storage and computational frameworks, with cloud-first considerations for Azure and AWS particularly welcome
    A history of compelling side projects or contributions to the Open-Source community is valued but not required
    Solid theoretical grounding in the mathematical core of the major ideas in data science
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