At Lifescale Analytics, we help achieve data-driven decisions by leveraging strategic technologies to create innovative solutions that ultimately help our clients navigate their ever-changing data landscape. We have an opportunity for a Data Scientist Machine Learning (ML) Engineer, candidates can be remote but must reside in the United States. The ideal candidate must have 5 years of experience identifying and documenting sources of component data and experience working with Machine Learning. The candidate will be responsible for analyzing data, developing algorithms, and working with developers and users to enhance operational systems.

Responsibilities:

Drive the system architecture and design decisions for machine learning infrastructure, for both cloud and on-premises environments, and for both batch and stream-based processing.
Design, develop, determine test strategy, test, and maintain key software improvements related to machine learning capabilities.
Work in a team with product managers and data scientists, as the software engineering technical leader, to develop novel solutions for our most difficult data-based challenges.
The ability to communicate model results as dashboards or charts and the ability to work with visualization teams to maximize the impact of your model.
Develop the technical vision and roadmap for the application.
Be a great teammate who enjoys collaborating with, learning from, mentoring, and teaching other team members to build a positive work environment.
Advise leadership on data science best practices and building data-driven organizations.
Recommend data architecture and schematics, ETL process/Tool, data governance, and potential integration with internal and external data, and analytic framework.
Responsibility for leading the design and development of complex GIS solutions.

Qualifications:

6+ years of experience as a Data Scientist with Machine Learning experience.
Proven track record of writing quality, well-documented and well-tested code for services in the AI/ML space.
Strong sense for the abstractions needed to represent ML processes with little to no tie-in to particular tools in the ever-changing machine learning software ecosystem.
Proven experience in high-performance computing, DevOps / MLOps.
Ability to write robust code in Python, Java, and R.
Deep knowledge of math, probability, statistics, and algorithms.
Experience in the application of best practices for software engineering and high-performance computing in the AI/ML and data spaces, and experience with helping others adopt them via code reviews or similar.
Highly accomplished in software engineering with intermediate to advanced skills in Python and experience standing up services.
In-depth understanding of modern ML platforms and backend systems.
Experience with R, SAS, AlterYX, or equivalent analytics framework.
Experience in analyzing, interpreting, and visualizing data.
Experience with ArcGIS, ENVI, QGIS, GDAL, or Python for geospatial data processing.
Experience with DevOps processes and tools such as Azure Pipelines and Azure Boards.
Helpful to have experience with AI software such as Tensorflow, PegaPlatform, Azure ML, Google Cloud AI, etc.

Education:

Bachelor’s degree in Computer Science or equivalent demonstrated work experience.

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Lifescale Analytics

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Data Scientist/Machine Learning Engineer