Silent Eight

• Drive multi-million dollar, multi-year deals through complex, multi-stakeholder sales cycles across European markets • Act as a strategic advisor to Chief Risk Officers (CROs) and Chief Compliance Officers (CCOs), aligning AI capabilities with their regulatory and risk priorities • Build and close pipeline through extensive travel across Europe (~70%) • Work with product and executive leadership to shape proposals that win • Collaborate with our teams in Asia and the US on global accounts with European presence

• Drive multi-million dollar, multi-year deals through complex, multi-stakeholder sales cycles across European markets • Act as a strategic advisor to Chief Risk Officers (CROs) and Chief Compliance Officers (CCOs) • Build and close pipeline through extensive travel across Europe (~70%) • Collaborate with our teams in Asia and the US on global accounts with European presence

• Go to the field: talk to users, shadow their workflows, capture the as‑is → goals & constraints. • Define hypotheses & KPIs (precision/recall, FPR, TAT, coverage, cost/decision) and turn them into experiment plans. • Design decision flows that mix LLMs, retrieval/RAG, classical ML, and lightweight rules; ensure explainability and auditability. • Build quick prototypes (notebook → lightweight service/API) and measure their impact on real data. • Create evaluation sets and scoring rubrics (offline + side‑by‑side + sanity checks + guardrails). • Present findings & recommendations directly to decision‑makers; propose rollout (pilot → production‑lite → scale). • Lead innovation processes across the company; test, promote solutions and mentor others with new AI technologies.

Role Description As a Senior Data Scientist, you will play a dual role: technical leader and strategic partner to customers and internal teams. - Lead complex R&D initiatives: drive research and innovation in LLMs, NLP, machine learning, and graph analytics, turning advanced techniques into production-grade solutions. - Own the full data science lifecycle: from discovery, framing, and exploration to deployment, customer delivery, and long-term monitoring. - Shape ambiguous challenges into well-defined problem statements and design practical, state-of-the-art solutions aligned with business and regulatory needs. - Engage directly with customers and stakeholders: collaborate to co-design use cases, set success metrics, and ensure solutions are adopted, measurable, and impactful. - Mentor and guide peers: elevate technical excellence, foster knowledge-sharing, and set best practices for experimentation, testing, and deployment. - Prototype rapidly: explore new ideas and validate hypotheses quickly, while balancing innovation with scalability and maintainability. - Ensure production success: design, implement, and optimize pipelines for data integration, validation, monitoring, and retraining to sustain long-term model performance. - Communicate effectively: distill technical complexity into compelling business insights, helping stakeholders make informed decisions. - Champion excellence in execution: enforce best practices in MLOps, DataOps, and software engineering to deliver reproducible, maintainable, and scalable solutions. - Continuously improve tooling and processes that accelerate delivery, experimentation, and collaboration across the data science function. Qualifications - Must be based in Singapore (remote-first team; flexible working environment). - Bachelor’s degree, Master or PhD in Data Science, Computer Science, Statistics, or related field. - 6+ years of hands-on experience delivering data science projects from research to production, ideally in mission-critical domains. - Proven expertise with LLMs and modern NLP frameworks (LangChain, LlamaIndex, OpenAI APIs, HuggingFace, etc.). - Strong foundation in machine learning, graph analytics, anomaly detection, and unstructured data workflows. - Advanced proficiency in Python and SQL with a track record of writing clean, modular, testable, and production-ready code. - Experience with large-scale data platforms (e.g., PySpark, BigQuery) and scalable ML deployments. - Solid knowledge of MLOps and DataOps, including CI/CD, monitoring, retraining, and reproducibility. - Comfortable in Linux, Git, Docker, and modern collaboration workflows. - Exceptional communication and stakeholder management skills: able to translate technical insights into business outcomes and influence at all levels. - Proven ability to drive measurable impact: balancing state-of-the-art research with practical, customer-driven implementation. - A mindset of ownership, flexibility, and resilience: you adapt quickly, handle ambiguity, and are committed to delivering results. Benefits - Competitive salary — 150,000 - 170,000 SGD per year - Equity participation — stock options that grow with us as we scale globally - Quarterly wellness allowance — spend it your way - Learning & development budget — training, conferences, and dedicated Development Days with our AI/ML team - The backing of a proven platform — AI already running in production at HSBC and Standard Chartered, so you're building something that works Company Description At Silent Eight, we develop our own AI-based products to combat financial crimes that enable things like money laundering, the financing of terrorism, and systemic corruption. We’re a leading RegTech firm working with large international financial institutions such as Standard Chartered Bank and HSBC. Join us and help make the world a safer place!

• Draft, review, and negotiate a broad range of commercial agreements, including NDAs, MSAs, software license and implementation agreements, DPAs, and support/maintenance contracts. • Provide timely, business-oriented legal advice to internal stakeholders (Sales, Product, Partnerships, Finance) and help balance risk with commercial objectives. • Oversee and implement compliance frameworks, draft and maintain global policies, and ensure adherence to data protection regulations (GDPR and other relevant jurisdictions). • Support the management of patents, trademarks, software, and licensing frameworks, while advising on IP protection and enforcement. • Advise on international legal matters and coordinate with local counsel when needed across Asia-Pacific, the UK, Europe, and the US. • Monitor developments in technology law, privacy, and AI regulations, and provide guidance on their impact on Silent Eight’s business.

Oto kompletna finalna wersja: Enterprise Sales Director – United States NYC About Us Silent Eight has been a trusted partner to the world's largest financial institutions for years — with AI running in production across global banks including HSBC and Standard Chartered. We automate compliance decisions that stop money laundering and terrorism financing, at scale and in production. We're expanding our US footprint and looking for the person to lead it. The Role You'll report directly to our CEO and own the full go-to-market for Tier 1 and Tier 2 financial institutions across the United States — from pipeline to close. We're looking for someone based in or near New York City. As the territory grows, so does your role. What You'll Do - Drive multi-million dollar, multi-year deals through complex, multi-stakeholder sales cycles - Act as a strategic advisor to CROs and CCOs, aligning AI capabilities with their regulatory and risk priorities - Work with product and executive leadership to shape proposals that win - Collaborate with our teams in Asia and Europe on global accounts with US presence What We're Looking For - 5+ years in enterprise sales, with 3+ years closing complex enterprise-grade software or high-stakes business solutions - Experience selling into regulated financial institutions - Domain knowledge in AML, FinCrime, or RegTech — strongly preferred - Ability to make sophisticated AI commercially tangible for non-technical buyers - Comfortable across global time zones; willing to travel Compensation - OTE: $300,000–$400,000 (base + variable) — per NYC Local Law 32 - Equity in the company - New York City preferred; remote-flexible Process - People Ops — intro (30 min) - Hiring Manager — sales strategy (45 min) - CEO conversation (30 min) - Panel presentation — enterprise case study

• Lead complex R&D initiatives: drive research and innovation in LLMs, NLP, machine learning, and graph analytics, turning advanced techniques into production-grade solutions. • Own the full data science lifecycle: from discovery, framing, and exploration to deployment, customer delivery, and long-term monitoring. • Shape ambiguous challenges into well-defined problem statements and design practical, state-of-the-art solutions aligned with business and regulatory needs. • Engage directly with customers and stakeholders: collaborate to co-design use cases, set success metrics, and ensure solutions are adopted, measurable, and impactful. • Mentor and guide peers: elevate technical excellence, foster knowledge-sharing, and set best practices for experimentation, testing, and deployment. • Prototype rapidly: explore new ideas and validate hypotheses quickly, while balancing innovation with scalability and maintainability. • Ensure production success: design, implement, and optimize pipelines for data integration, validation, monitoring, and retraining to sustain long-term model performance. • Communicate effectively: distill technical complexity into compelling business insights, helping stakeholders make informed decisions. • Champion excellence in execution: enforce best practices in MLOps, DataOps, and software engineering to deliver reproducible, maintainable, and scalable solutions.

• Develop and maintain Python-based applications and tools that support our AML solutions • Implement, optimize, and deploy machine learning models, including large language models, to enhance our product capabilities • Collaborate with data scientists and cross-functional teams to integrate machine learning models into production systems • Design and implement data integration, validation, and quality check frameworks • Work on the entire machine learning pipeline, from data preprocessing to model deployment and monitoring • Create quick prototypes and present them to customers and internal stakeholders to drive product development • Analyze and process large-scale structured and unstructured data sets to support machine learning initiatives • Ensure the robustness, scalability, and efficiency of the developed applications and models • Provide technical support and troubleshoot issues related to machine learning models and Python applications • Follow best practices in coding, MLOps, and DataOps • Assist other teams in delivering proof-of-concept projects

• Lead data exploration and analysis on large scale financial crime datasets — including sanctions, PEP (Politically Exposed Persons), and adverse media data — to uncover patterns, identify false positives/negatives, and drive feature improvements. • Develop and evaluate agents and rule-based models by running experiments, validating hypotheses, and fine-tuning thresholds to improve alert efficiency. • Build and deliver production-ready API integrations — coordinating with software engineers and product teams to ensure components are properly integrated, tested, and merged. • Conduct customer-focused data studies across multiple enterprise clients (e.g., financial institutions) to benchmark model performance, assess data quality, and propose data driven solutions to reduce investigation loads. • Prototype and iterate quickly — using PySpark, Jupyter notebooks, and Python to explore data, build reproducible pipelines, and generate insights that inform product decisions. • Investigate and resolve product issues in collaboration with engineering and product teams. • Contribute to R&D on emerging techniques — including graph-based approaches (GNNs, graph embeddings) for transaction monitoring, LLM-based feature exploration, and RAG-based models. • Communicate findings clearly through well-organized Jupyter notebooks, internal documentation, and stakeholder presentations, translating complex analytical results into actionable business insights.

• Deploy and configure Silent Eight solutions in customer environments (SIT, UAT, PROD) • Troubleshoot and resolve technical issues (including escalated service desk tickets) • Perform Root Cause Analysis (RCA) • Collaborate with Solution Architects, Project Managers, and client IT teams • Participate in optimizing internal processes and tooling • Support DevOps-related tasks, including CI/CD automation • Contribute to the creation and maintenance of technical documentation • Be flexible to support urgent escalations (Severity 1 incidents) outside of regular business hours