At a Glance
- Tasks: Design challenging computational problems to test AI's ability in scientific research.
- Company: Join a pioneering team focused on advanced AI and scientific problem-solving.
- Benefits: Flexible hours, remote work, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and creativity.
- Why this job: Make a real impact by shaping the future of AI in science and engineering.
- Qualifications: PhD or MS with extensive experience in statistics or applied mathematics.
The predicted salary is between 59400 - 72600 £ per year.
Computational Statistics and Applied Mathematics Expert
About the Project
We're building a large-scale benchmark to test how well advanced AI systems can solve hard scientific and engineering problems. As a task designer, you'll create challenging computational problems that check whether AI can use real scientific software to do research-level work — running simulations, interpreting results, designing experiments, and uncovering hidden information from data. This isn't a typical data-labeling job. You'll design original, graduate-level problems based on real scientific workflows, test them against cutting-edge AI models, and fine-tune them until the difficulty is just right.
What You'll Do
You'll create problems that require skilled use of specialized statistical, mathematical, or scientific software packages. Some will ask the AI to compute reproducible numerical answers from a fully defined setup — testing whether it can correctly carry out complex, multi-step workflows. Others will be harder: the AI must plan a series of queries or experiments to uncover information that isn't directly visible, which means thinking strategically about what to measure, how to read partial results, and how to narrow down the possibilities efficiently. Each problem goes through a testing loop against state-of-the-art AI models, and you'll refine it until it hits the target difficulty.
Domains & Tools We're Hiring For
We welcome statisticians and applied mathematicians working across a wide range of specializations. You do not need experience with every package listed below; strong expertise with one or more specialized computational packages is sufficient. We're especially interested in experts with deep, hands-on experience using one or more specialized R or Python packages, including examples such as:
- Bayesian statistics: rstan, cmdstanr, rjags, runjags, brms, rstanarm, nimble, bayesplot, posterior, loo
- Item response theory and psychometrics: TAM, sirt, mirt, mirtCAT, eRm, ltm, lordif, psych
- Structural equation and latent variable modelling: lavaan, semTools, OpenMx
- Topological data analysis: TDAstats, TDApplied
- Differential equations and dynamical systems: deSolve, pomp, FME
- State-space and time-series modelling: KFAS, MARSS, forecast, vars, urca, rugarch, rmgarch, tseries, timeSeries
- Survival and event-history analysis: survival, flexsurv, timereg, mets
- Mixed, additive, and advanced regression models: lme4, nlme, mgcv, glmmTMB, TMB, quantreg, scam
- Spatial statistics and geostatistics: spatstat, spatstat.geom, spatstat.linnet, spdep, gstat, geoR, spBayes, sf, stars, terra, lwgeom
- Statistical learning and specialized modelling: mclust, kernlab, earth, pROC, multcomp, sandwich, effectsize, irr
- Optimization and mathematical programming: lpSolve, linprog, nloptr, DEoptimR, SQUAREM
- Numerical linear algebra and high-precision computation: RSpectra, Rmpfr, gmp, pracma
- Computational geometry: geometry, deldir, polyclip
Other similar specialized statistical, mathematical, scientific, or domain-specific R packages will also be considered. Other similar specialized statistical or mathematical Python/Scilab packages are also welcome, such as statsmodels and PyMC. Numerical computing and scientific modelling in Matlab/Scilab are also wanted.
What Makes a Strong Candidate
You have graduate-level expertise (MS or PhD required; PhD preferred, or MS with 10+ years of relevant experience) in statistics, applied mathematics, or a closely related quantitative field, with real hands-on experience using specialized computational packages — not just theoretical knowledge. You have written code using one or more specialized statistical, mathematical, or scientific packages to solve actual research or professional problems, and you understand where these tools break, what their edge cases are, and what makes a problem genuinely hard rather than just complicated. Deep expertise with one or more specialized computational packages is more important than familiarity with the entire package list above. Beyond domain expertise, the best candidates think like puzzle designers: building problems where the challenge comes from smart reasoning rather than raw computation, where several approaches seem plausible but only careful analysis reveals the right one, and where surface-level pattern matching won't get you to the answer.
Requirements
- Graduate-level training in statistics, applied mathematics, a relevant STEM field, or equivalent research experience
- Proven proficiency with at least one specialized statistical, mathematical, or scientific software package, demonstrated through research publications, open-source contributions, or professional work
- Strong Python skills — you'll be writing problem setups, oracle functions, and solution validators
- Ability to work independently and refine problem designs based on feedback
- Comfortable working in a Linux/terminal environment with remote compute sandboxes
- Available for at least 15–20 hours per week
Nice to Have
- Experience across multiple computational domains or specialized software packages
- Familiarity with benchmark or evaluation design
- Background in scientific teaching or exam/problem-set design
- Experience with computational reproducibility and containerized environments
Computational Statistics Expert - PhD employer: Obsidian
Obsidian is an exceptional employer located in the vibrant Greater London area, offering a dynamic work culture that fosters innovation and collaboration among experts in the field. Employees benefit from a fast-start program with opportunities for growth and extension, alongside a commitment to quality in AI model training that makes a meaningful impact in genomics. With a focus on professional development and a supportive environment, Obsidian is dedicated to empowering its team members to excel in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Computational Statistics Expert - PhD
✨Get Involved in Data Challenges
Participate in data challenges like Kaggle competitions or DrivenData to showcase your skills and network with other data enthusiasts. Not only will you build your portfolio, but you can also catch the eye of potential employers like Obsidian.
✨Connect with Local Data Communities
Join local data science meetups or online communities like Data Science Society to engage with professionals in the field. These platforms are great for networking, discovering job opportunities, and keeping your fingers on the pulse of industry trends.
✨Leverage Your University’s Resources
If you're still in university, make full use of your career services. They might have part-time roles tailored for students like you, and often have direct connections with companies looking to hire talented interns in data science roles.
✨Apply Directly Through Our Website
Don’t forget to check out our jobs at Obsidian and apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate individuals like us who are eager to make an impact in the data science world.
We think you need these skills to ace Computational Statistics Expert - PhD
Some tips for your application 🫡
Show Your Data Skills:In your CV, make sure to highlight your proficiency with key data analysis tools and programming languages like Python, R, or SQL. We want to see that you've got hands-on experience with data manipulation and visualisation, so if you've worked on any relevant projects or coursework, include those details to really showcase your skills!
Tailor Your Projects Towards Data Science:When it comes to your portfolio, focus on showcasing projects that highlight your data-science abilities. Include analyses, dashboards, or any predictive models you've built. If you've contributed to Kaggle competitions or have a GitHub repository with data projects, make sure to link those—these demonstrate your practical experience and problem-solving abilities.
Express Your Motivation in the Cover Letter:Since this is a part-time role, we want to know why you're particularly interested in juggling this with your other commitments. Use your cover letter to express your passion for data science and how this role at Obsidian aligns with your career aspirations. Show us you're excited about learning and growing with us!
Keep It Concise Yet Informative:Part-time positions often receive many applications, so keep your documents clear and to the point! Aim for a concise CV detailing your relevant experiences without unnecessary fluff. Be sure to include your availability in your cover letter as well—that helps us in the decision-making process!
How to prepare for a job interview at Obsidian
✨Brush Up on Your Stats!
Given you're eyeing a part-time role in data science, make sure you’re on top of your statistical methods and data analysis techniques. Expect questions around regression, hypothesis testing, and maybe even some statistical programming languages like R or Python during the interview with Obsidian.
✨Show Off Your Projects!
It's crucial to have a portfolio that showcases your data science projects. Highlight your part-time work with specific data sets, models you've built, or analyses you've conducted. Having tangible examples will demonstrate your hands-on experience and problem-solving skills to Obsidian.
✨Familiarise Yourself with Tools of the Trade
Make sure you’re well-versed in data science tools like Jupyter Notebook, Tableau, or SQL. You might get technical questions or even a practical test at Obsidian, so having a comfort level with these tools will definitely be an advantage.
✨Be Ready to Discuss Real-World Applications
Since this is a part-time role, employers at Obsidian will likely appreciate your understanding of how data science can address actual business problems. Be prepared to discuss any relevant case studies or how you would approach specific challenges in real scenarios.
