Careers > Senior Data Scientist

Position: Senior Data Scientist

JobID: DS001
Team: Data Science Consulting

Locations: All Offices, Remote. 

The target locations for this position are: Boston, New York, San Francisco, Portland, Denver, Washington, D.C. or remote in neighboring areas. 

Please send cover letter, CV or resume to
careers@sflscientific.com or apply below.

Description

SFL Scientific is expanding our data science consulting team and we are seeking highly skilled, creative, and driven senior and lead data scientists to design and build breakthrough approaches and machine learning solutions across all industries.

As a Senior Data Scientist, you will be work directly with our clients to deliver data-driven solutions for a whole range of data science and data engineering projects - healthcare, biotech, pharma, manufacturing, legal, supply chain, etc. Our ideal candidate is an accomplished expert with deep technical ability and implementation skills – he/she designs, prototypes, develops, and fields predictive models and analytical approaches that power critical business functions. 

The successful candidate will have demonstrated ability to manage projects, provide thought leadership, and act as an autonomous member of the SFL Scientific team, while reporting directly to senior leadership. Strong communication and presence, collaboration, and ability to operate effectively in a fast moving environment are key success factors. The role of the Senior Data Scientist will include the entire workflow of a data science project: From ensuring data quality throughout all stages of acquisition and processing to the cleaning, visualizing, analyzing, predicting with the data, and creation of a final product or pipeline.
        
Job Role & Strengths:

Strong primary expertise as data engineer or data scientist, with the ability to stretch beyond one’s core field of expertise. Successful candidates share the following requirements:

  • Track record of diving into data to discover hidden patterns and conducting error/deviation analysis

  • High-level knowledge of various machine learning techniques and key parameters that affect their performance

  • Ability to develop experimental and analytic plans for data modeling processes

  • Use of strong baselines, ability to accurately determine cause and effect relations

  • Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.

  • Ability to think creatively and solve problems

  • Strong client management skills – ability to lead and persuade, positive energy, relentless focus on business impact

Core Requirements:

  • Bachelor’s or Master’s degree in a relevant field

  • 5+ years experience with various data analysis and visualization tools

  • Proficient in a core programming language, such as: Python, C/C++, Scala, Java, Ruby

  • Proficient in Python or R, particularly to prototype mathematical and machine learning models

  • Proficient with SQL and/or NoSQL

  • Proficient with Tableau, R-Shiny or other data visualization tools

Preferred Qualifications:

  • Proficient with AWS or Azure cloud computing environments

  • Proficient with a distributed computing platform (Hadoop, Spark, etc.)

  • Experience querying and administering big data storage services (Redshift, Teradata, Aurora, DynamoDB, etc.)

  • Experience with general software release cycle, productionizing ML or predictive analytics models at scale

You must be authorized to work in the US. We support flexible work hours and paid professional development. 

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About SFL Scientific

SFL Scientific is a US-based data science consulting and services company, providing a broad range of services and solutions in data engineering, big data, machine learning, and Artificial Intelligence. SFL works at the intersection of business and technology to help clients improve their performance and create sustainable value for stakeholders using custom algorithm development and data-driven solutions. We are industry agnostic and instead focus on solving complex, R&D, and challenging business problems.