Data Scientist
Location
United States
Posted
1 day ago
Salary
$120K - $155K / year
No structured requirement data.
Job Description
Data Scientist
Remote, USA
$120,000 - $155,000 annually + Benefits
Must be a US citizen, No sponsorship available
5+ years’ experience
Actionable Outcomes (AO) is a dynamic and innovative Counter Fraud Analytics and Software Development Company that's dedicated to creating advanced technologies that allow our clients to fight fraud faster and more efficiently. With decades of fraud experience, our goal is to help businesses achieve their strategic goals through actionable insights and tailored solutions.
Our team of experienced professionals brings a wealth of knowledge in various industries, enabling us to provide customized strategies that drive measurable results. At Actionable Outcomes LLC, we believe in the power of data-driven decision-making and are committed to delivering exceptional value to our clients. Whether you are looking to optimize operations, enhance customer experience, or drive growth, we are here to turn your vision into reality.
We are seeking Data Scientist that is reliable, enthusiastic, and a team player. This role involves working closely with stakeholders, data engineers, analysts, and domain experts to develop transparent, explainable, and defensible analytical solutions in a regulated environment. The ideal candidate has strong analytical skills, experience working with complex datasets, and the ability to clearly communicate insights and recommendations to both technical and non-technical audiences.
Primary Qualifications:
- 5+ years of professional experience performing data science or advanced analytics work
- Strong proficiency in Python or R and SQL
- Experience applying statistical methods and machine learning techniques (e.g., regression, classification, clustering, time series)
- Experience working with large, complex, or structured datasets
- Ability to clearly communicate analytical findings to non-technical stakeholders
- Ability to work independently and collaboratively in a remote environment
- Familiarity with working in regulated or compliance-driven environments
- Experience with data visualization and BI tools (e.g., Power BI, Tableau)
- Experience supporting use cases such as fraud detection, risk analysis, program integrity, performance measurement, or policy analysis
- Understanding of data governance, privacy, and responsible AI concepts
- Certifications (a plus): cloud analytics certifications, data science certifications, or related credentials
- Candidates may have experience with the following tools or technologies:
- Languages: Python, R, SQL
- Libraries/Frameworks: pandas, NumPy, scikit-learn, statsmodels, TensorFlow/PyTorch
- Visualization: Power BI, Tableau, matplotlib, seaborn
- Platforms: Cloud or hybrid analytics environments such as AWS, Azure, GCP
- Collaboration: Microsoft Teams, SharePoint, Jira, Confluence
Responsibilities include:
- Apply statistical analysis, data mining, and machine learning techniques to address operational needs
- Develop, validate, and deploy predictive, descriptive, and diagnostic models in support of program objectives
- Perform exploratory data analysis (EDA) to identify trends, patterns, anomalies, and risk indicators
- Collaborate with data engineering teams to define data requirements, feature engineering approaches, and analytic pipelines
- Ensure analytical methods and outputs are explainable, reproducible, and auditable
- Develop and document model assumptions, methodologies, limitations, and performance metrics
- Support use cases such as forecasting, risk scoring, anomaly detection, classification, and segmentation
- Create visualizations, dashboards, and analytical summaries to communicate findings to stakeholders
- Support validation, testing, and continuous monitoring of models to ensure ongoing performance and compliance
- Produce high-quality documentation, technical memos, and briefings in accordance with federal standards
- Participate in Agile/Hybrid delivery processes, including sprint planning, reviews, and status reporting
Benefits:
- Paid time off – 11 holidays plus vacation and sick time.
- Health & Dental Insurance, voluntary employee-paid Vision Insurance.
- 401k – Our 401k plan matches up to 3.5% of your income (SIMPLE IRA capped at 3%).
Location
United States (Remote)
Department
Recruiting
Employment Type
Full-Time
Minimum Experience
Mid-level
Compensation
$120,000 - $155,000
Related Guides
Related Categories
Related Job Pages
More Data Scientist Jobs
Data Scientist, Innovation Lab - Remote
ExperianWe're unlocking the power of data to help create a better tomorrow.
The Data Scientist will create advanced machine learning analytical solutions to extract insights from diverse structured and unstructured data sources and refine data manipulation through efficient data structure design. They will also dissect and document vast datasets, analyze them to highlight patterns, and solve complex challenges by developing impactful algorithms.
Lead Data Manager
Tempus AITempus is advancing data-driven precision medicine with the practical application of AI in healthcare. It’s About Time.
Senior Clinical Data Manager designing and executing clinical evidence studies for cancer care
The Associate Director/Director Biostatistician will provide strategic and technical leadership for statistical activities across multiple clinical trials, leading the development and review of key statistical deliverables like protocols, SAPs, and regulatory submission materials. This role involves serving as the key statistical representative in cross-functional meetings while ensuring all statistical approaches adhere to regulatory standards and best practices.
Senior Data Scientist, Innovation Lab - Remote
ExperianWe're unlocking the power of data to help create a better tomorrow.
The role involves creating advanced machine learning analytical solutions to extract insights from diverse structured and unstructured data sources, and unearthing data value by selecting and applying the right machine learning, deep learning, and processing techniques. Responsibilities also include refining data manipulation, innovating with data processing tools, dissecting datasets, developing impactful algorithms, and ensuring model excellence through validation and documentation.