Role Description As a Machine Learning Data Scientist – Research Translation & Prototyping, you will work at the intersection of cutting-edge AI research and real-world application. You will partner with researchers, engineers, designers, and product stakeholders to evaluate emerging technologies, build rapid prototypes, and develop machine learning solutions that help transform experimental concepts into usable tools and experiences. This role is ideal for a hands-on builder who thrives in fast-paced, ambiguous environments and enjoys translating research, data, and novel ideas into measurable outcomes. Success requires strong technical expertise in machine learning and software engineering, the ability to design and execute experiments, and a passion for quickly validating new technologies through prototyping and evaluation. Key Responsibilities - Collaborate with research, engineering, and cross-functional teams to evaluate emerging AI and machine learning technologies and determine their practical value. - Design, develop, and implement machine learning models, AI-powered applications, and experimental systems. - Build rapid prototypes and proof-of-concept solutions to validate new technologies and research concepts. - Fine-tune, benchmark, validate, and improve machine learning models using real-world datasets. - Develop evaluation frameworks, benchmarks, and success metrics for AI systems, foundation models, generative AI solutions, multimodal experiences, and agent-based workflows. - Design and execute quantitative and qualitative experiments to assess model performance, user engagement, technology adoption, and overall effectiveness. - Analyze system requirements, document technical specifications, and develop software solutions aligned with project objectives. - Gather, process, and analyze data to generate actionable insights and support decision-making. - Evaluate, troubleshoot, and improve machine learning pipelines, AI systems, and software implementations. - Develop, test, and maintain software applications and supporting infrastructure. - Create and execute test plans, perform unit testing, and support quality assurance efforts. - Support deployment, validation, and post-implementation monitoring of solutions, resolving issues identified during testing and rollout. - Stay current with advancements in machine learning, generative AI, multimodal systems, agentic workflows, and related research areas to identify opportunities for innovation and application. Qualifications - Bachelor's degree in Computer Science, Computer Engineering, Data Science, Mathematics, Statistics, or a related technical field. - 5–7+ years of professional experience in machine learning, data science, applied AI, software engineering, or a related discipline. - Strong experience developing machine learning models and AI-powered solutions. - Demonstrated experience with data science methodologies, experimentation, model evaluation, and statistical analysis. - Hands-on software engineering experience, including coding, debugging, testing, and deployment. - Experience building data-intensive applications, machine learning systems, experimentation platforms, or AI-enabled products. - Strong programming skills and the ability to diagnose and resolve technical issues. - Experience evaluating, improving, and maintaining machine learning models, data pipelines, and AI applications. - Ability to quickly learn new technologies, adapt to changing priorities, and contribute effectively in ambiguous, fast-moving environments. - Strong communication skills with the ability to explain technical concepts and findings to both technical and non-technical audiences. - Experience working collaboratively across research, engineering, product, and business teams. Preferred Qualifications - Experience translating research concepts, academic publications, or emerging technologies into working prototypes and production-ready solutions. - Experience with foundation models, large language models (LLMs), generative AI systems, multimodal AI, agentic workflows, and retrieval-augmented generation (RAG). - Proven ability to rapidly prototype and iterate on ideas using modern AI development tools and AI-assisted coding workflows. - Experience designing evaluation frameworks, benchmarks, and success metrics for AI systems. - Familiarity with model fine-tuning, experimentation, model validation, and performance optimization techniques. - Experience working on research-driven initiatives or innovation-focused environments. - Ability to ramp up quickly on new projects and deliver meaningful results within short timelines. - Experience supporting end-to-end machine learning solution development, from experimentation through deployment and validation. - Demonstrated flexibility and success working across multiple research or product domains simultaneously. - Availability for a long-term engagement (12+ months preferred). Salary Range Pay ranges vary based on multiple factors including, without limitation, skill sets, education, responsibilities, experience, and geographical market. The pay range for this position reflects geographic based ranges for Washington state: $145,000- $155,000 USD annually. The salary/wage and job title for this opening will be based on the selected candidate’s qualifications and experience and may be outside this range. Benefits - Medical, dental, and vision coverage - Flexible Spending Account - 401k program - Competitive PTO offerings - Parental Leave - Opportunities for professional growth and development Equal Opportunity Employer Blueprint Technologies, LLC is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, age, disability, sex, gender identity or expression, orientation, veteran/military status, religion, national origin, ancestry, marital, or familial status, genetic information, citizenship, or any other status protected by law. If you need assistance or a reasonable accommodation to complete the application process, please reach out to: recruiting@bpcs.com Location Remote

• Collaborate with research, engineering, and cross-functional teams to evaluate emerging AI and machine learning technologies and determine their practical value. • Design, develop, and implement machine learning models, AI-powered applications, and experimental systems. • Build rapid prototypes and proof-of-concept solutions to validate new technologies and research concepts. • Fine-tune, benchmark, validate, and improve machine learning models using real-world datasets. • Develop evaluation frameworks, benchmarks, and success metrics for AI systems, foundation models, generative AI solutions, multimodal experiences, and agent-based workflows. • Design and execute quantitative and qualitative experiments to assess model performance, user engagement, technology adoption, and overall effectiveness. • Analyze system requirements, document technical specifications, and develop software solutions aligned with project objectives. • Gather, process, and analyze data to generate actionable insights and support decision-making. • Evaluate, troubleshoot, and improve machine learning pipelines, AI systems, and software implementations. • Develop, test, and maintain software applications and supporting infrastructure. • Create and execute test plans, perform unit testing, and support quality assurance efforts. • Support deployment, validation, and post-implementation monitoring of solutions, resolving issues identified during testing and rollout. • Stay current with advancements in machine learning, generative AI, multimodal systems, agentic workflows, and related research areas to identify opportunities for innovation and application.

Role Description Leads the data-science and risk-analytics workstream supporting Safeguards' risk-based review selection. Builds and maintains the models, scripts, and dashboards that rank partner agencies by risk and inform which agencies are selected for review. Key Responsibilities - Maintain and operate data fields, code, and scripts that analyze review-finding data and its relationship to Risk Model outputs and Health Score calculations. - Revise and maintain formulas and macros that provide weighted evaluations of risk factors. - Perform data aggregation to identify the most “at-risk” agency partners, including manual validation of automated rankings. - Develop and maintain the Risk Dashboard — findings, risk levels, rankings, remediation status, and historical trends — with clear visualizations for the Risk Team. - Support the Quarterly Risk Team Meetings and maintain the Risk Support Work Plan. Qualifications - Demonstrated experience applying scientific methods, processes, algorithms, and systems to extract or extrapolate insights from structured and unstructured data across multiple application domains. - Demonstrated use of automation and scripting techniques. - Experience presenting analytic results to management and non-technical personnel. Requirements - Minimum Education: Bachelor's degree or higher in accounting, mathematics, engineering, finance, economics, and/or data science from an accredited college or university. - Specialized Skills: - Tableau - R programming - Python 3 - VBA - Citizenship: US Citizen (MUST) - Security Clearance: Must be eligible to possess MBI (IRS Background Investigation) clearance. Active IRS MBI clearance is preferred. Commitment to Diversity eTelligent Group provides equal employment opportunities (EEO) to all applicants without regard to race, color, religion, gender, sexual orientation, gender identity, nations origin, age, disability, genetic information, marital status, amnesty, status as a covered veteran, and any other characteristic provided in accordance with applicable, federal, state and local laws.

• Aid with the statistical design, analysis, and reporting of clinical study data (pre-market feasibility, pre-market pivotal, post-market, observational, and RWE) • Collaborate with clinical, medical, and regulatory teams to ensure study objectives are met with robust statistical methodologies • Develop innovative data science approaches (e.g., predictive modeling, ML/AI) for clinical trial optimization and patient outcomes research • Oversee data quality, integrity, and compliance with regulatory standards (FDA, EMA, ICH-GCP) • Partner with Data Management teams on CRF design, data cleaning, and database lock activities • Ensure data traceability, reproducibility, and adherence to relevant data standards • Provide data science input to clinical development plans, study protocols, and statistical analysis plans (SAPs) • Support publications, abstracts, and presentations with high-quality deliverables • Mentor clinical affairs interns • Represent the company in scientific forums, regulatory discussions, and cross-industry initiatives.