We are currently seeking a talented Data Scientist to join us as we develop our data driven, analytic focused and predictive products to help revolutionize the law enforcement sector. As a Data Scientist, you’ll get to:
- Build, review, and refine machine learning models related to improving the management of law enforcement officers;
- Build data pipelines for extracting thousands of variables across 10s/100s of millions of records;
- Collaborate with the Benchmark Research Team and other employees to build/enhance data tools/models
If you are interested in the intersection of public sector operations, research and data analytics, we believe this is an unprecedented opportunity to experience it first-hand.
- Build, improve, and validate machine learning and statistical models
- Select models for publishing to production clients
- Build and implement data pipelines to transform/reshape data
- Write, review, and maintain code and configurations (SQL/JSON/etc) for performing data models
- Review pipeline processes, identify bugs and inefficiencies, suggest improvements
- Prepare presentations and reports based on the results of work/analysis
- Build and improve data visualizations to understand model performance and results
- Create process documentation as necessary to prepare the team and company for growth/scale
- Collaborate with other data scientists and analysts to improve work product
- Work with stakeholders to drive decisions and measure progress
- Build and review test cases for modelling processes and pipelines
- Educate and learn from internal teams on best practices, relevant knowledge and specific skills
- Work with the research, product, and engineering teams to iteratively improve results
The ideal candidate will be someone who has:
- PhD in quantitative field or computer science preferred, but all applicants considered based on education, experience and demonstrated skills (eg Kaggle project)
- A zeal for building, refining, and delivering machine learning models
- Experience building and selecting machine learning models (eg, logistic regression, decision trees, random forest, SVM, etc)
- Experience in building data pipelines that include flat files, structured files, and relational databases
- Experience in Exploratory Data Analysis
- Experience in coding/scripting for statistical modeling (e.g., Python, SciKitLearn, R, SQL, etc.)
- Strong sense of process (ability to understand how steps relate to each other to achieve end results)
- Strong data and numeric sense (central tendency, patterns vs noise, etc)
- Able to learn and adapt to new technologies quickly
- The ability to successfully manage multiple concurrent tasks/projects and meet deadlines
- Ability to anticipate challenging situations and proactively plan appropriate actions;
- Ability to work independently with minimal guidance from management (except in complex and non-routine situations)
- Strong sense for working and communicating well with others:
- Has empathy for colleagues and customers
- Able to receive and respond positively to feedback
- Able to work independently with minimal guidance from management (except in complex and non-routine situations);
- Able to effectively communicate across multiple levels (customers, team members, managers) in a fast-paced environment
What we offer:
- A truly unique experience to work for a fast-growing startup
- We cover 75% of Medical Premiums, Dental & Vision Premiums, and offer 100% company sponsored Life Insurance.
- Flexible PTO policy, 401k retirement savings plan and 3% employer contribution.
- The opportunity to transform law enforcement in the US.
The fine print and how to apply:
- Thank you for your interest in our company and what we are building.
- Benchmark Analytics is an Equal Opportunity Employer. We value diversity of all kinds in our effort to create a stellar workforce of committed and passionate team members.
- Unfortunately, we are not able to sponsor employment visas at this time, so we can only accept applications from candidates who are authorized to work in the US.
- If you’d like to apply, please email your resume to email@example.com.