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Data Scientist and Analytics Engineer

SherlockTalent • Remote (NY, United States) • $200k - $250k / year • 1m ago

Job Title: Data Scientist and Analytics Engineer

Location: 100% REMOTE

Job Type: Perm Full-Time

Salary: $120K – $170K Depending on Experience

Job ID: 7170

Our client runs a High-Performance Computing Platform (HPC) on AWS along with a multitude of open-source technologies and middleware. The systems run in the Cloud, so we always think cloud first! Our team uses Linux and some Windows. We are removing barriers that keep the product team from executing faster than our competitors and releasing a clean, quality product. This means supporting & testing our stack in a public cloud as well as with distributed schedulers, logging solutions, metrics, storage archiving, and optimization of HPC application cost & performance.

About the Job

The candidate will work closely with HPC engineers to build reusable components for generating real-time insights and analytics for HPC simulations.
The candidate will also assist in Research & Development for our next generation machine learning products in Network Simulations.
The candidate is expected to have experience working with data pipelines and ML frameworks.

Minimal Requirements

Experience using statistical computer languages (Python, Golang, SQL, etc.) to manipulate data and draw insights from large data sets:
- 1+ years working with Python programming.
- 1+ year working with SQL (Postgresql).
Knowledge and experience in machine learning algorithms:
- Random Forest, boosting, decision trees, clustering.
- LSTM, Reinforcement learning.
1+ years working with scikit-learn, or related machine learning framework.
1+ years working with Tensorflow, Pytorch, or related deep learning framework.
1+ years working with bokeh, holoviews, or related visualization library.
1+ years working with a RDBMS.

Responsibilities Include (Not Limited To):

Working with internal teams and clients to develop machine learning applications.
Run machine learning tests and experiments.
Extend existing ML libraries and frameworks when necessary.
Study and transform data science prototypes.
Develop processes and tools to monitor and analyze model performance and data accuracy.
Develop an A/B testing framework and test model quality.

Preference Will Be Given To Candidates With The Following:

A drive to learn and master new technologies and techniques.
Familiar with development and deployment on Cloud environment (AWS CLI, Boto3, Docker, etc.).
Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, etc.
Experience with CUDF and other CUDA-based libraries.

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