Artem Dinh

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: • Owning end-to-end product delivery, including a strategic data integration project: defined partner API requirements, tested endpoints via Python, and drafted technical specifications. • Bridging leadership vision and engineering execution by translating stakeholder needs into technical specs (PRDs, user flows) and executive roadmaps; built AI prototypes (Python, LLM APIs) to validate R&D hypotheses. • Leading beta execution through structured testing and backlog prioritization; collaborated with engineers within React codebase to refine UI/UX and improve frontend delivery. • Owning the technical due diligence workstream, coordinating technical materials and responses under strict IP disclosure constraints. 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀: • Reduced engineering overtime by around 50%, decreased development time by approximately 30%, and improved delivery estimate accuracy to near 90% by eliminating rework and tightening requirements. • Delivered the product to Beta, deploying to 20 active contributors across 50+ real-world cases, and launching the company’s first mobile app. • Completed technical due diligence in a three-month window, enabling the M&A deal to close under strict deadlines, resolving 200+ technical inquiries and structuring 300+ IP-protected artifacts in a secure data room to ensure audit readiness. • Reviewed 44 IP and open-source licensing modules and delivered evidence accepted by the buyer and attorneys.

Led day-to-day Agile delivery for cross-timezone teams: turned law-enforcement user needs into epics/user stories & acceptance criteria, prioritized backlog in Jira, and coordinated QA/UAT and release readiness (incl. first mobile app publishing pipeline).

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: • Analyzed large volumes of multilingual textual data under tight deadlines, combining automated reporting with parallel manual NLP-based review to ensure quality, accuracy, and continuous improvement. • Partnered with ML engineers to review checkpoint outputs, troubleshoot workflow issues, and standardize analyst QA and editing protocols. • Owned third-party data vendor integrations, leading source onboarding, prototyping API integrations, and providing continuous monitoring and feedback. • Developed modular Python scripts to clean, filter, and format unstructured data for downstream analysis in LLM and model-driven workflows. 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀: • Cut analytics turnaround time by 50% by introducing checkpoint-based reviews with ML engineers. • Developed and launched end-to-end data ingestion pipelines for large-scale OSINT monitoring, capturing 85% of relevant media signals across two strategic regions. • Over a 12-week iteration cycle, minimized critical review comments from editors and customers by improving sources, keywords, and quality controls.

• Conducted regulatory, reimbursement, and competitive analysis across 6 countries and 15 competitors, delivering executive-ready market entry recommendations. • Supported fundraising and go-to-market efforts by representing the company in 3 accelerators and identifying 600+ relevant investment leads using automation and structured market research.

• Trained and fine-tuned TensorFlow CNN models for pedestrian detection and body-part classification on low-resolution images using transfer learning and model adjustments. • Tripled the dataset with a custom augmentation strategy and improved F1 score from 40% to 65%+ by refining labeling protocols and labeling 1,000+ images with the annotation team.

𝗖𝗼𝘂𝗿𝘀𝗲𝘄𝗼𝗿𝗸: Big Data, Data Science Privacy, Privacy Law, International Communication, Systems Analysis, Cyber Policy, AI/ML DevOps (Harvard University)

Senior Design Project: "𝘐𝘯 𝘷𝘪𝘵𝘳𝘰 𝘉𝘰𝘯𝘦 𝘙𝘦𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘜𝘴𝘪𝘯𝘨 𝘛𝘳𝘪-𝘊𝘶𝘭𝘵𝘶𝘳𝘦 𝘚𝘤𝘢𝘧𝘧𝘰𝘭𝘥𝘴" 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝘂𝗿𝘀𝗲𝘄𝗼𝗿𝗸: Data Structures, Numerical Methods, Analytical Tools. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘄𝗼𝗿𝗸: Medical Instrumentation Design, Engineering Design Process, Biophotonics, 3D Printing & Biopriting. 𝗢𝘁𝗵𝗲𝗿: Scientific Writing & Presentation, Ethics, Ukraine's Foreign & Security Policy.