Lead Data Scientist

Published on Jul 06, 2021

Make it in UA

A Product company with office in Kyiv, Ukraine.

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Experience:

5+ years

Job Type:

Full-Time

Remote

Specializations:

Data Engineering

Deep Learning

Data Science & AI

Overview Project & team

As the first pan-industry platform built on the Internet Computer (ICP), generate new value opportunities for some of the largest consumer asset classes in the world, including art, digital media and luxury goods.

Founded in October 2020, operates globally in major technology and blockchain hubs, including its research and development division located in Manhattan Beach, California.

For the first time in history, an object itself constitutes its own unique identity.

What You Will Do Responsibilities

A Lead Data Scientist will be responsible for addressing the immediate needs around data collection and organization, as well as running experiments in order to validate key assumptions.

What You Should Bring

Must-have

● Python experience
● Prior experience with machine learning or computer vision is a big plus, but may not be strictly necessary if you are able to learn fast and understand relevant technical documentation.

Nice-to-have

Engineering Management
● Building and leading a team
● Coordinating work between engineers and data scientists
● Making major technical decisions
● Reporting to other executives, adjusting strategies

Data Science
● Collecting and organizing data
● Running experiments, tracking results
● Pipelines for ergonomic algorithm exploration
● Validating assumptions, aligning overarching goals

DSP and Information Theory
● Target signal sources
● How to best encode signals
● Liaise with robotics/hardware
● ROI/signature/feature extraction

Computer Vision and Image Processing
● Image preprocessing
● Image matching/transformations
● ROI/signature/feature extraction
● non-ML full solutions

Machine Learning and CV in ML
● ML model selection
● Training and deployment
● Hyperparameter tuning
● ML pipelines/full solutions

Software Engineering
● Production architecture
● Component I/O
● Production deployment
● Developer tools and codebase organization

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