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Bayesian Modeling Jobs in New York (NOW HIRING)

Oxman

Computational Ecologist

New York, NY · On-site

$152K - $203K/yr

Strong foundation in modeling techniques (e.g., differential equations, agent-based modeling, network models, Bayesian approaches) for simulating ecological processes or population dynamics.

CMP.jobs

Sr. Machine Learning Engineer, AdTech

Manhattan, NY

$61.25 - $81.25/hr

... existing models of event predictions; - New feature engineering for multiple machine learning ... Bayesian inference) - ML & DS (e.g., dimensionality reduction, geometry of PCA / SVD and of L1 / L2 ...

CyberCoders

Data Scientist

New York, NY

You love building mathematical and probabilistic models, computer science algorithms, and you have ... Experience with statistical methods and analysis such as bias vs. variance tradeoffs, Bayesian ...

CyberCoders

Data Scientist

New York, NY · On-site

You love building mathematical and probabilistic models, computer science algorithms, and you have ... Experience with statistical methods and analysis such as bias vs. variance tradeoffs, Bayesian ...

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Bayesian Modeling information

What is the difference between Bayesian Modeling vs Data Scientist?

AspectBayesian ModelingData Scientist
Required CredentialsStatistics, Mathematics, Data AnalysisStatistics, Computer Science, Data Analysis
Work EnvironmentResearch-focused, statistical modelingCross-functional, data analysis, visualization
Industry UsageResearch, academia, specialized analyticsBusiness, tech, finance, healthcare
Common Search/ComparisonYesYes

Bayesian Modeling and Data Scientists often overlap in skills like statistics and data analysis. Bayesian Modeling specializes in probabilistic models and statistical inference, while Data Scientists have broader roles including data cleaning, visualization, and machine learning. Both roles are essential in data-driven industries, but Bayesian Modeling is more focused on advanced statistical techniques.

What are the key skills and qualifications needed to thrive as a Bayesian Modeler, and why are they important?

To thrive as a Bayesian Modeler, you need a solid background in statistics, probability theory, and mathematical modeling, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as R, Python, or Stan, and experience with statistical software and Bayesian inference tools are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with multidisciplinary teams. These skills ensure accurate model development, reliable data-driven insights, and clear communication of complex findings to stakeholders.

How does a Bayesian Modeling specialist typically collaborate with cross-functional teams in a workplace setting?

Bayesian Modeling specialists often work closely with data scientists, software engineers, and domain experts to integrate probabilistic models into larger analytical or production systems. They are involved in translating complex statistical concepts into actionable insights and recommendations tailored to business needs. Effective communication is key, as they must present findings to both technical and non-technical stakeholders, ensuring that model assumptions and results are clearly understood. Collaboration may also include contributing to code reviews, sharing best practices for model validation, and mentoring colleagues on Bayesian methodologies.

What is Bayesian modeling?

Bayesian modeling is a statistical approach that uses Bayes' Theorem to update the probability of a hypothesis as more data becomes available. It incorporates prior beliefs or knowledge, combines them with observed data, and produces a posterior probability distribution to guide inference and decision-making. This approach is widely used in various fields such as machine learning, data science, and scientific research for tasks like parameter estimation, prediction, and model selection.
What are popular job titles related to Bayesian Modeling jobs in New York? For Bayesian Modeling jobs in New York, the most frequently searched job titles are:
What job categories do people searching Bayesian Modeling jobs in New York look for? The top searched job categories for Bayesian Modeling jobs in New York are:
What cities in New York are hiring for Bayesian Modeling jobs? Cities in New York with the most Bayesian Modeling job openings:
Bayesian Modeling jobs near you
Computational Ecologist

Computational Ecologist

Oxman

New York, NY • On-site

$152K - $203K/yr

Full-time

Re-posted 22 days ago

Job description

OXMAN
OXMAN is a nature-based research and design company based in Manhattan. We incubate ventures and technologies that reimagine the relationship between humanity and the natural world. Working across disciplines-from architecture and ecology to materials science and computation, we develop nature-centric solutions to critical environmental challenges.
EDEN
Nature provides humanity with services that are critical for survival: the sequestration of carbon, the filtration of water, and the production of the air we breathe. EDEN works to strengthen and regenerate these natural processes by cultivating biodiverse, resilient ecosystems that sustain life for all species-human and non-human alike.
EDEN is a digital design environment for engineering and designing ecosystems, modeling the flows, relationships, and processes that sustain them. We build tools that quantify how landscapes can be engineered to achieve specific performance goals, cooling cities, filtering water, sequestering carbon, and protecting key species, and use them to guide the design of ecologically active sites.
One hectare of well-designed landscape can sequester up to four times the annual emissions of an average home, filter enough water to support thirteen neighborhoods, and reduce ambient temperatures by more than ten degrees. EDEN enables designers to plan intentionally for these outcomes through analysis, simulation, and optimization, turning ecological function into an actionable design parameter.
Our design team works directly with clients to apply these tools toward site-specific goals, from logistics campuses and residential communities to rewilding and climate-resilient developments. Together with our clients, we are designing biodiverse, productive environments that serve both humanity and nature.
Key Responsibilities
  • Conceptualization and Research: Research and identify key ecosystem behaviours and interactions to create a comprehensive conceptual framework for general ecosystem modelling
  • Ecosystem Behaviour Modelling: Leading of the modelling of core ecosystem dynamics and interactions as defined in the conceptualization phase such as plant growth and succession etc.
  • Ecosystem Metrics Development: Development of quantitative metrics to assess ecosystem health, stability, and service provision.
  • Implementation and Documentation: Models will be implemented in a computationally efficient framework with thorough documentation to ensure usability and reproducibility.
  • Help in the gathering and integration of relevant environmental, ecological, and spatial data to underpin model parameters and validate model outcomes.
  • Conduct data analysis to in order to derive key insights necessary to develop ecosystem models and validate model parameters.
  • Technical documentation: Prepare comprehensive documentation outlining model assumptions, data sources, code structure, and operation for using and maintaining the models.
  • Continually communicate with the OXMAN team to ensure review of research, implementation, and seamless integration of the model into their workflows.
  • Participate in regular progress meetings (weekly or biweekly) with the team to review milestones, discuss challenges, and plan next steps.
  • Provide status updates summarizing progress, challenges encountered, and any adjustments to the project plan.
Key Goals and Outcomes
  • Development, deployment, and validation of a general ecosystem model within two quarters of the start date
Required Experience
  • A Ph.D. or equivalent experience in Computer Science, Computational Ecology, Systems Biology, or a related field.
  • Strong programming skills in languages such as Python, C++, or similar, with experience in frameworks like PyTorch, or JAX.
  • Strong foundation in modeling techniques (e.g., differential equations, agent-based modeling, network models, Bayesian approaches) for simulating ecological processes or population dynamics.
  • Experience handling large and often messy datasets common in ecology (e.g., climate data, remote sensing imagery, biodiversity records). Knowledge of spatial databases, parallel computing, or cloud-based data storage is a plus
Technical Skills
  • Python (NumPy/SciPy/pandas), reproducible research workflows, and Git-based version control
  • High-performance model implementation (vectorization, profiling/optimization); familiarity with PyTorch or JAX
  • Ecological modeling methods: agent-based, ODE/PDE, network, and Bayesian/statistical modeling; uncertainty quantification
  • Geospatial analytics (GIS; GeoPandas/rasterio/GDAL) and spatial databases (e.g., PostGIS) for integrating environmental and biodiversity data
  • Remote sensing and gridded data handling (e.g., xarray; land cover/land use change; climate rasters); comfort with messy real-world datasets
  • Clear technical documentation (assumptions, data provenance, APIs) and maintainable code (testing, modular design)
Essential Qualities
  • Systems-level thinker who can translate ecological theory into tractable computational abstractions
  • Strong research judgment: literature synthesis, hypothesis framing, and disciplined model validation
  • Pragmatic engineer: prioritizes computational efficiency, robustness, and reproducibility over "toy" prototypes
  • Comfortable working with uncertainty, noisy data, and incomplete ground truth, typical of ecological problems
  • Clear communicator in interdisciplinary teams (design, biology, engineering); proactive stakeholder management
  • High ownership: independently drives milestones while aligning work to EDEN workflow integration needs

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