Intermediate Data Scientist

Applications will be accepted through Friday, October 4, 2024.

With a 110-year heritage, the National Insurance Crime Bureau (NICB) is the nation's premier not-for-profit organization dedicated exclusively to fighting insurance fraud and crime.

NICB membership includes more than 1,200 property-casualty insurance companies, vehicle rental companies, auto auctions, vehicle finance companies, self-insured organizations and strategic partners. Beyond our membership, our nearly 400 employees work with law enforcement agencies, technology experts, government officials, prosecutors, international crime-fighting organizations and the public to lead a united effort to prevent and combat insurance fraud and crime.

A career with the National Insurance Crime Bureau (NICB) is rewarding and fulfilling to people who have the passion to play an important role in combating insurance fraud and vehicle crime. At the NICB, we know our people are our competitive advantage, and so, we offer a work atmosphere that is second to none.

Position Purpose:

The Intermediate Data Scientist will be a major contributor in determining future technologies, advocating for best practices, and enforcing advanced analytics standards. As both a data and model steward, the position will stay up to date with local and federal rules and regulations with regards to security, data privacy, and governance as NICB takes the protection of individuals and our NICB members (law enforcements, insurance carriers, and other members) very seriously. The Intermediate Data Scientist will have the opportunity to present and work with other NICB leaders throughout the organization.

Work Arrangement and Description:

This is a remote position which means NICB grants the privilege and flexibility to work from a location within the boundaries of the United States or its Territories.

Competencies/Duties:

• Conduct exploratory analysis from complex and disparate data sources to recognize patterns and identify current or potential instances of insurance fraud
• Review, evaluate and communicate the appropriateness of modeling techniques and outcomes to different levels of both non-technical and technical audiences as part of the model deployment and approval process
• Leverage advanced analytics techniques to identify improvement opportunities for predictive and prescriptive reporting and collaborate with the IT Department to implement enhancements
• Serve as an expert resource on best practices/approaches for statistics, machine learning techniques, data modeling, simulations, and advanced mathematics
• Understand the business problems and develop advanced machine learning techniques to build models that address business needs and provide insights and actionable recommendations
• Participate in building best practices for model governance, management, and model optimization
• Embed automated processes for model development life cycle and other NICB products and services, including model optimization capabilities, value quantification, and model management/governance functionalities
• Collaborate with analyst and agent cadres to ensure findings are communicated as actionable intelligence using the appropriate approach both internally to NICB and externally to the membership and partners, as appropriate
• Document all processes, research, and outcomes, including reviews and approvals
• Provide consultation to NICB leadership, member companies, and law enforcement partners on how to best leverage analytics to achieve desired outcomes
• Other duties as assigned

Position Requirements/Abilities:

• Ability to build, optimize, and deploy real-time predictive models
• Experience in programming with Python and choosing the appropriate ML algorithms such as Linear Regression, Random Forest, and XGBoost
• Experience choosing and utilizing the appropriate ML libraries such as Scikit-learn and PyTorch
• Experience with relational databases and NoSQL databases and their supporting languages such as SQL and JSON
• Experience in Agile methodologies and version control practices and tools such as Git
• Working experience in data cleaning and processing libraries such as NumPy and Pandas
• Experience using both structured and unstructured data (i.e., computer vision, NLP, voice analysis, etc.)
• Experience in managing and manipulating large, complex datasets and ability to analyze and interpret moderate to complex concepts
• Extensive experience in diagnosing, isolating, and resolving complex business problems and recommending and implementing strategies to resolve problem
• Experience working with graph and graph analysis, including ETL operations and visualization of graphs preferred
• Experience with cloud-based ecosystems, ideally AWS tools such as SageMaker, Lambda, EMR, etc. preferred
• Working knowledge of common supervised and unsupervised machine learning algorithms
• Knowledge of data science best practices, including familiarity with data governance and model governance
• Experience with unstructured data such as computer vision, NLP, voice analysis, etc.
• Experience working with web development tools like Streamlit, Flask, or Django preferred.
• Familiarity with DevOps tools (such as Docker) and practices preferred
• Solid capability in researching, assessing, learning, and using a wide variety of tools and languages to achieve results
• Independent strategic thinking rooted in initiative
• Refined time management, ability to meet deadlines, and the ability to work effectively with minimal supervision
• High degree of organization, ability to multitask, and adaptability to change
• Sound decision making, problem solving, and judgement
• Experience working with non-technical experts to derive key needs and requirements
• Guide team members to ensure that best practices are being followed, including privacy policies
• Strong interpersonal abilities and coalition building acumen
• Strong verbal and written communication skills

Education, Work Experience, Licensure:

• Bachelor s degree required, degree in Computer Science, Data Science, or other computational discipline preferred and 5 or more years of relevant experience or
• Master s degree in Computer Science, Data Science, or other computational discipline and 3 or more years of relevant work experience required
• 3 or more years experience working with Python (for machine learning, data wrangling, data engineering, web development, or process automation) and SQL in an academic or professional setting required.
• 2 or more years experience in machine learning required
• Relevant AWS certifications preferred
• Insurance experience, specifically with insurance fraud preferred

Supervisory Responsibilities:

This position has no supervisory responsibilities

Contacts:

• NICB Employees Across All Departments
• Technology Vendors
• Membership Companies
• Law Enforcement and Intelligence Community Partners
• Industry Associations

Environment/Physical Demands

• Minimal travel may be required

Salary Range and Statement:

The salary range for this position is $102,000 to $139,000. Please keep in mind that this range represents the pay range for all positions in the job grade within which this position falls. The actual salary offer will take into account a wide range of factors including the specific requirements for this position, the candidate's geographic location, job-related skills, experience, and relevant education or training. NICB HR can share more about the specific salary range for your geographic location during the hiring process.

Benefits:

Our benefits are unparalleled! NICB offers employees a comprehensive benefits package, an attractive 401(k) plan and generous paid time off. For more information, visit www.nicb.org/careers.

The NICB is an Equal Opportunity Employer and Prohibits Discrimination and Harassment of Any Kind: NICB is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. All employment decisions at the NICB are based on business needs, job requirements and individual qualifications, without regard to race, color, religion or belief, national, social or ethnic origin, sex (including pregnancy), age, physical, mental or sensory disability, sexual orientation, gender identity and/or expression, marital, civil union or domestic partnership status, past or present military service, family medical history or genetic information, family or parental status, or any other status protected by the laws or regulations in the locations where we operate.