{"id":1002504,"url":"https://alion.io/job/hermeneutic-quantitative-researcher","title":"Quantitative Researcher","company":{"id":720326,"name":"Hermeneutic","domain":"hermeneutic.com","url":"https://alion.io/company/hermeneutic-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":88,"open_postings":8,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["TW"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":"Time Series","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-09-17T13:29:19Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-11-05T13:14:03Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":49},"description":"Company Overview\nhermeneutic research is a best-in-class proprietary trading firm. It deploys research-driven discretionary and systematic strategies as well as makes strategic long-term investments. The partners' decade-long history of success in trading and business building and a firm-wide cultural emphasis on alpha generation, open debate, relentless iteration, and teamwork are key to the firm's continued expansion in a challenging market environment that has hamstrung competitors. A hard-wired emphasis on risk management and opportunistic market participation ensures that hermeneutic research will continue its growth trajectory in the coming decades.\nJob Overview\nAs a Quantitative Researcher at hermeneutic research, you will develop and improve systematic trading strategies across digital asset markets.\nYou will work across the full research lifecycle: identifying market inefficiencies, developing hypotheses, analyzing large-scale market data, building predictive signals and trading models, designing robust backtests, and helping to monetise successful research and improve firm-wide execution quality.\nThis role will be grounded in real market behavior and production trading data. You will work extensively with tick market data, order book and trade data, execution data, and portfolio-level performance to identify market inefficiencies, test hypotheses, and determine whether observed patterns are robust and tradable.\nYou will join a team with high-frequency/systematic trading and market making background, whose strategies that naturally enable improvement of firm-wide execution quality. You will collaborate closely with traders, quantitative researchers, and engineers while retaining significant ownership of your own research. Successful researchers are expected not only to develop sophisticated models, but also to identify the questions that matter, design rigorous experiments, challenge assumptions, and translate findings into economically meaningful improvements to live trading.\nThis is an opportunity for a strong quantitative researcher to work in a highly collaborative environment with access to substantial data, technology and trading infrastructure.\nKey Responsibilities\nResearch, develop and optimize systematic trading strategies and signals across digital asset markets.\nAnalyze high-frequency market data, including trades, order books, derivatives data, and cross-venue market activity, to identify exploitable market structure and behavioral patterns.\nFormulate research hypotheses and design statistically rigorous experiments to evaluate them.\nBuild robust backtesting and simulation frameworks while carefully accounting for transaction costs, market impact, latency, liquidity, and other real-world trading constraints.\nResearch market microstructure, liquidity dynamics, price formation, execution behavior, and short-horizon alpha.\nDevelop quantitative models for signal generation, execution, portfolio construction, and risk management.\nEvaluate existing strategies and identify opportunities to improve alpha, execution quality and robustness.\nWork closely with engineers to translate successful research into reliable production trading systems.\nMonitor live strategy performance and investigate discrepancies between research, simulation, and production results.\nContinuously improve research methodologies, datasets, tooling, and experimental standards across the quantitative research process.\nRequirements\nMust-Haves\nA degree in Mathematics, Statistics, Computer Science, Physics, Engineering, Finance, or another highly quantitative field.\nStrong quantitative and statistical reasoning, with the ability to translate ambiguous market questions into testable hypotheses.\nStrong programming ability, particularly in Python, with experience analyzing large datasets and building quantitative research pipelines.\nExperience conducting empirical research using financial, market, or similarly noisy real-world datasets.\nStrong understanding of probability, statistics, time-series analysis, and quantitative modeling.\nAbility to distinguish statistically interesting results from economically meaningful and tradable opportunities.\nStrong attention to research methodology, including robustness testing, avoiding look-ahead bias and overfitting, and correctly evaluating out-of-sample performance.\nIntellectual curiosity and the ability to independently investigate complex problems while collaborating effectively with others.\nExcellent English communication skills and the ability to clearly explain research methodology, results, limitations, and implications.\nPreferred Qualifications\nPrior experience in quantitative trading, systematic investing, market making, or high-frequency trading.\nExperience researching market microstructure, execution, order books, transaction costs, or short-horizon price dynamics.\nExperience working with tick-level or high-frequency financial data.\nFamiliarity with digital asset markets, derivatives, perpetual futures, and fragmented multi-venue market structure.\nExperience developing signals or strategies that have been deployed into live trading.\nExperience with machine learning methods applied to financial markets.\nInterview Process\nCV and Research Experience Screening - We will review your academic background, quantitative experience, and evidence of strong empirical research ability.\nHR Interview - If further clarification is needed, a brief call may be scheduled to better understand your background and motivations.\nQuantitative Research Interview - A discussion focused on probability, statistics, markets, research methodology, and how you approach open-ended quantitative problems.\nResearch Case Study / Technical Interview - A practical exercise designed to assess your ability to analyze data, formulate hypotheses, evaluate evidence, and communicate conclusions.\nFinal Interview - A concluding discussion to assess your research judgment, intellectual curiosity, strategic thinking, and cultural fit.\nCompany Values\nThroughout the process, you'll be assessed for cultural fit through our company values:\nDrive - We believe the best team members are deeply passionate about what they do. That passion fuels their growth, drives them to seek out the best teams, and leads them to hold high expectations of themselves in pursuit of excellence.\nOwnership - We aim to extend ownership as broadly as possible across the firm. In return, we value people who take initiative, step up when needed, and treat the company's goals as their own.\nJudgment - We value those who see the big picture and focus on what truly drives impact and results. They use time wisely, adapt across domains, and always prioritize outcomes over comfort zones.\nOpenness - We foster a culture of open communication, mutual challenge, and shared growth. We believe constructive debate and proactive knowledge sharing lead us closer to the truth-and better decisions.\nCompetence - We work with people who bring exceptional intellectual strength. What sets them apart isn't just what they know-it's how they use what they know to navigate change, adapt quickly, and contribute meaningfully in fast-changing environments.\n Resilience - We perform under pressure. Markets move fast, and so do we-but we stay grounded, focused, and calm. 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