Data Scientist and Analytics Engineer - New York, United States - SherlockTalent

Description

Job Title:
Data Scientistand Analytics Engineer

Location:100% REMOTE


Job Type:
Perm Full-Time

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

Job:7170

Our client runs a High-Performance Computing Platform (HPC) on AWS along with a multitude of opensource technologies and middleware. The systems run in the Cloud so we always think cloud first Our team usesLinux 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 forHPC simulations

The candidate will also assist in Research & Developmentfor 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 (AWSCLI, Boto3, Docker and etc)

Experience with distributed data/computing tools:
Map/Reduce, Hadoop, Hive, Spark and etc

Experience with CUDF and other CUDA-based libraries

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