6 Bayesian Modeling Jobs

As the Director of Data Modelling and Insights at Mastercard, you will play a crucial role in partnering with the Product & Process Transformation team to enhance analytical insights and develop robust data models that drive product execution and strategic decision-making. Your responsibilities will involve leveraging your expertise in Excel, Power Query, and Power BI to structure, clean, and transform data into actionable dashboards, forecasts, and performance metrics. In this position, you will be expected to build and maintain data models using Power BI to facilitate informed decision-making across various product and process initiatives. By utilizing Power Query, you will extract, transform, and cleanse extensive datasets from multiple systems. Additionally, you will design and implement DAX measures to compute metrics, monitor performance, and uncover critical business insights. Your role will also entail leading in-depth analytical investigations into product velocity, pipeline friction, and development timelines through timestamp analysis. Furthermore, you will be responsible for developing Bayesian forecasting models to predict outcomes such as launch timing, completion risk, or capacity shortfalls. Constructing crosstab analyses and experimental frameworks to evaluate the impact of interventions or process modifications will also be part of your duties. Collaboration with cross-functional teams, including product managers, engineers, and finance leads, will be essential to embed data-driven decision-making into everyday operations. Your ability to define measurement frameworks and establish meaningful targets that drive accountability will be instrumental in shaping the company's strategic direction. The ideal candidate for this role holds a Bachelor's or Master's degree in Data Science, Engineering, Mathematics, Economics, or a related field, coupled with at least 8 years of experience in data analytics, business intelligence, or applied statistical modeling. You should possess advanced proficiency in Microsoft Excel, including Power Query and advanced functions, as well as in Power BI for data modeling, DAX, and dashboard creation. Demonstrated experience in applying statistical and probabilistic methods, such as Bayesian modeling, within a business context is highly desirable. Strong communication skills, the ability to translate analytical findings into actionable recommendations, and a passion for exploring patterns to make data-driven decisions are key attributes for success in this role. You should be a highly organized systems thinker who can distill complexity into structured models and clear insights. A proactive collaborator who excels in cross-functional environments, building trust through accuracy and timely delivery, will thrive in this position at Mastercard. As a representative of Mastercard, it is imperative that you adhere to the organization's security policies and practices, ensuring the confidentiality and integrity of the information accessed. Reporting any suspected information security violations or breaches and completing all mandatory security trainings are essential responsibilities for all individuals working for or on behalf of Mastercard.,

You are looking for a Lead Data Scientist with over 10 years of experience in the field. The ideal candidate should have a Master's or PhD in Computer Science, Mathematics, Statistics, or a relevant field. In this role, you will be responsible for leading the development of next-generation Analytics and Machine Learning software. Your primary responsibilities will include utilizing your strong expertise in Statistics, Mathematics, and Engineering to spearhead the development process. Experience in Bayesian modeling and Probabilistic modeling is essential. You should also have experience working with Python and one of the popular probabilistic programming systems such as Pyro, PyMC, or Stan. Additionally, machine learning programming experience in Python and PyTorch is required. The successful candidate will possess exceptionally strong analytical skills and excellent problem-solving abilities. You should be able to effectively translate business problems into Data Science or NLP problems. In-depth knowledge of Statistics, Machine Learning, LLM's, GenAI, and experience in creating visualizations for Data are also crucial for this role. Strong communication skills, both technical and day-to-day, are essential. If you meet the specified requirements and are interested in this exciting opportunity, please send your updated resume to drishyar@chiselontechnologies.com.,

The job involves designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Building simulation frameworks grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Developing systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Researching fractal intelligence structures, swarm-based agent coordination, and autopoietic systems. Advancing Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integrating logic into distributed decision graphs aligned with business and ethical constraints. Publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory. Building scalable simulations of multi-agent ecosystems within the Mobius runtime. You should have a Ph.D. or M.Tech in Artificial Intelligence, Cognitive Science, Complex Systems, Applied Mathematics, or equivalent experience. Proven expertise in meta-learning, recursive architectures, and AI safety. Strong knowledge of distributed systems, multi-agent environments, and decentralized coordination. Proficiency in formal and theoretical foundations like Bayesian modeling, graph theory, and logical inference. Strong implementation skills in Python, additional proficiency in C++, functional or symbolic languages are a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is required. Preferred qualifications include experience with neurosymbolic architectures, hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, distributed learning systems like Ray, Spark, MPI, or agent-based simulators. Technical proficiency required in Python, preferred in C++, Haskell, Lisp, or Prolog for symbolic reasoning. Familiarity with frameworks like PyTorch, TensorFlow, distributed systems like Ray, Apache Spark, Dask, Kubernetes, knowledge technologies including Neo4j, RDF, OWL, SPARQL, experiment management tools such as MLflow, Weights & Biases, GPU and HPC systems like CUDA, NCCL, Slurm, and formal modeling tools like Z3, TLA+, Coq, Isabelle. Core research domains include recursive self-improvement and introspective AI, graph theory, graph rewriting, knowledge graphs, neurosymbolic systems, ontological reasoning, fractal intelligence, dynamic attractor-based learning, Bayesian reasoning, cognitive dynamics, swarm intelligence, decentralized consensus modeling, topos theory, autopoietic system architectures, teleo-dynamics, and goal-driven adaptation in complex systems.,

You are an individual contributor at Adobe Advertising Cloud, which offers a Demand Side Platform (DSP) and a Spend Optimizer to assist customers in planning, buying, measuring, and optimizing their digital media across various platforms. The primary goal is to enable global advertisers to enhance the effectiveness of their paid media budgets by delivering connected and personalized experiences to consumers. Your role will be within the Spend Optimizer group, working alongside a team of dedicated data scientists and engineers. Together, you will utilize techniques such as reinforcement learning, time series analysis, and bayesian modeling, along with Gen AI frameworks, to optimize ad spends effectively. Your responsibilities will include identifying and developing innovative experiences for Ad Cloud customers, leveraging upcoming AI technologies to shape the product roadmap, leading a team of machine learning engineers, collaborating with cross-functional teams to define technical requirements, deploying scalable machine learning solutions, and driving innovation through research and experimentation. To succeed in this role, you must possess a deep understanding of machine learning frameworks such as PyTorch, Tensorflow, and Scikit-learn, along with strong programming skills in Python. Proficiency in core machine learning concepts, statistics, and AI is essential, including expertise in Bayesian Modeling, Time Series Analysis, Reinforcement Learning, Optimization, and Deep Learning. Additionally, experience in developing and maintaining ML models in a production environment and the ability to thrive in a collaborative and diverse workplace are crucial. The ideal candidate will have over 10 years of experience in applied Machine Learning settings, hold a PhD or Masters in Computer Science/Applied Math/Statistics, and exhibit domain expertise in Digital Ad Technologies. Strong problem-solving skills, adaptability, and the ability to influence technical and non-technical collaborators are key qualities for success in this role. Internal growth opportunities are encouraged at Adobe, with a focus on creativity, curiosity, and continuous learning. You are advised to update your Resume/CV and Workday profile, explore the Internal Mobility page on Inside Adobe, and prepare for interviews. If you apply for a role, the Talent Team will contact you within 2 weeks, and you should inform your manager if you progress to the interview stage. Adobe provides an exceptional work environment, fosters ongoing feedback through the Check-In approach, and offers meaningful benefits. If you are seeking to make a positive impact, Adobe is the place for you. For further assistance or accommodation due to a disability or special need during the application process, contact accommodations@adobe.com or call (408) 536-3015.,

You will be responsible for designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Your role will involve building simulation frameworks for behavior grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Additionally, you will develop systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Conducting research on fractal intelligence structures, swarm-based agent coordination, and autopoietic systems will be part of your responsibilities. You are expected to advance Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integration of logic into distributed, policy-scoped decision graphs aligned with business and ethical constraints is crucial. Furthermore, publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory will be part of your duties. Lastly, building scalable simulations of multi-agent, goal-directed, and adaptive ecosystems within the Mobius runtime is an essential aspect of the role. In terms of qualifications, you should have proven expertise in meta-learning, recursive architectures, and AI safety. Proficiency in distributed systems, multi-agent environments, and decentralized coordination is necessary. Strong implementation skills in Python are required, with additional proficiency in C++, functional, or symbolic languages being a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is also desired. Preferred qualifications include experience with neurosymbolic architectures and hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, and distributed learning systems. In terms of technical proficiency, you should be proficient in programming languages such as Python (required), C++, Haskell, Lisp, or Prolog (preferred for symbolic reasoning), frameworks like PyTorch and TensorFlow, distributed systems including Ray, Apache Spark, Dask, Kubernetes, knowledge technologies like Neo4j, RDF, OWL, SPARQL, experiment management tools like MLflow, Weights & Biases, and GPU and HPC systems like CUDA, NCCL, Slurm. Familiarity with formal modeling tools like Z3, TLA+, Coq, Isabelle is also beneficial. Your core research domains will include recursive self-improvement and introspective AI, graph theory, graph rewriting, and knowledge graphs, neurosymbolic systems and ontological reasoning, fractal intelligence and dynamic attractor-based learning, Bayesian reasoning under uncertainty and cognitive dynamics, swarm intelligence and decentralized consensus modeling, top os theory, and the abstract structure of logic spaces, autopoietic, self-sustaining system architectures, and teleo-dynamics and goal-driven adaptation in complex systems.,

Responsibilities : Serve as expert in Machine Learning, develop framework to build Production grade AI/ML models. Apply AI/ML to accelerate business innovation and solve impactful business problems for our clients. Partner with business and technical stakeholders to translate challenging business problems into state-of-the-art data science solutions. Present results, insights, and recommendations to senior management with an emphasis on the business impact. Build engaging rapport with client leadership through relevant conversations and genuine business recommendations that impact the growth and profitability of the organization. Required Skills: Deep knowledge in AI/ML algorithms e.g. Regression, Classification, Clustering, Time Series, Graph Network, Recommender System, Bayesian modeling, Deep learning, Computer Vision, NLP/NLU, Reinforcement learning, Federated Learning, Meta Learning. Proficient in using some of the following techniques for business problems/projects: Linear & Logistic Regression, Decision Trees, Random Forests, K-nearest neighbors, Support Vector Machines ANOVA, Principal Component Analysis, Gradient Boosted Trees, ANN, CNN, RNN/LSTM. Hands-on in programming languages Python/R, Spark or Scala, SQL. Hands-on in AI/ML frameworks and libraries (e.g. TensorFlow, Keras, PyTorch). Hands-on in Azure cloud AI/ML services. Expert in Statistical Modelling & Algorithms e.g. Hypothesis testing, Sample size estimation, A/B testing. Good knowledge in Mathematical programming - Linear Programming, Mixed Integer Programming etc., Stochastic Modelling - Markov chains, Monte Carlo, Stochastic Simulation, Queuing Models. Some experience with Optimization Solvers (Gurobi, Cplex) and Algebraic Programming Languages(PulP). Experience in deploying and monitoring classical ML models in production, delivering data products to end-users, good knowledge in DevOps/MLOps - CI/CD pipelines. Good to have experience in data processing frameworks e.g. Apache Spark, Hadoop, Databricks.