• Machine Learning Engineer

Industry Accounting/Finance
Location Iasi
Experience Range 3 - 7 Years
Qualification BE Computer Science and Engineering (Computer Science)
Open

Functional Finance
Job Description
About Us
We know that people are our "greatest asset". Our staff’s professionalism, innovation, teamwork and dedication to excellence have helped us become one of the world’s leading technology companies. It is these qualities that are vital to our continued success. As a Ness employee you will be working on products and platforms for some of the most innovative software companies in the world. The opportunity to evolve your expertise by using new cutting edge technologies will expand your horizons and create an exciting work environment. You’ll also gain enormous knowledge working alongside other highly skilled professionals that will help accelerate your career progression. At Ness we treat our values of rigor, innovation and partnership with the highest priority and they are placed at the very core of our business — to guide us through our daily operations and interactions with our customers. We offer our employees exciting and challenging projects across a diverse range of industries, as well as the opportunity to collaborate with a group of forward thinking, capable partners around the globe.
About Company
Confidential
Roles and Responsibility

*Open position for Ness Iasi and Ness Timisoara with the possibility to work remote

As a ML Engineer you will work on multiple data science projects in collaboration with internal and external project owners on the product, commercial, and data team. You will be responsible for providing machine learning engineers support, create data pipeline for modeling, scale models, develop APIs to help move machine learning models in productions. You will collaborate with the data scientists and production-oriented software engineers


The Team:

You will be part of a rapidly growing organization, joining the team of highly motivated and professional Data Scientists and Machine Learning Engineers. The client provides financial and industry data, research, news and analytics to investment professionals, government agencies, corporations, and universities worldwide. They integrate news, comprehensive market and sector-specific data and analytics into a variety of tools to help clients track performance, generate alpha, identify investment ideas, understand competitive and industry dynamics, perform valuations, and assess credit risk.


What’s in it for you:

We provide a highly inclusive work environment where in you can bring your whole self to work to assist in achieving the client's mission of being one of the leading providers of the highest quality risk evaluations and analytical information to the world’s financial markets. As an integral part of our team, you will be working on cutting edge state-of-the-art technology stack. In this role, you will work on multiple data science projects in collaboration with internal and external project owners on the product, commercial, and data team. You will be responsible for providing machine learning engineers support, create data pipeline for modeling, scale models, develop APIs to help move machine learning models in productions. You will collaborate with the data scientists and production-oriented software engineers.

Desired Skills

• University degree in engineering or a similarly technical field 

• Fluency programming in Python and Javascript 

• Comprehensive Typescript / React UI/UX development experience 

• Strong written and verbal communication skills 

• Demonstrated understanding of applied machine learning 

• Familiarity with typical ML Libraries e.g. Sklearn 

• Deep Learning experience with either Tensorflow or PyTorch 

• Code-first data exploration and visualization 

• Systems thinking and an Entrepreneurial spirit 

Preferred qualifications

• Graduate degree in a technical field 

• Strong working knowledge of: 

• Kubernetes 

• CI/CD and infrastructure as code 

• Queues and data pipelines e.g. Kafka 

• SQL and or Spark data engineering 

• Experience with MLOps-specific tools such as MLFlow 

• Experience releasing and supporting production applications in a cloud environment 

• Spark Streaming and data lake expertise 

• Experience owning a production machine learning application throughout its entire life cycle 

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