... Bayesian Inference, Hidden Markov Models, Generalized ARMA, or Kalman Filtering is a plus. * Experience in non-linear optimisation including Simulated Annealing, Genetic Algorithm, Agent Based ...
... Bayesian Inference, Hidden Markov Models, Generalized ARMA, or Kalman Filtering is a plus. * Experience in non-linear optimisation including Simulated Annealing, Genetic Algorithm, Agent Based ...
Senior Data Scientist
Madison, WI · On-site
... Bayesian Inference, Hidden Markov Models, Generalized ARMA, or Kalman Filtering is a plus. * Experience in non-linear optimisation including Simulated Annealing, Genetic Algorithm, Agent Based ...
Senior Data Scientist
Madison, WI · On-site
... Bayesian Inference, Hidden Markov Models, Generalized ARMA, or Kalman Filtering is a plus. * Experience in non-linear optimisation including Simulated Annealing, Genetic Algorithm, Agent Based ...
Bayesian Optimization information
What is the difference between Bayesian Optimization vs Data Scientist?
| Aspect | Bayesian Optimization | Data Scientist |
|---|---|---|
| Primary Focus | Optimizing complex functions and hyperparameters | Analyzing data, building models, deriving insights |
| Required Skills | Statistics, probability, machine learning, programming | Statistics, programming, data analysis, visualization |
| Work Environment | Research labs, AI/ML teams, R&D departments | Business, tech companies, consulting firms |
| Common Tools | Python, R, Bayesian libraries (e.g., GPy, scikit-optimize) | Python, R, SQL, visualization tools |
Bayesian Optimization is a specialized technique used within machine learning and AI to efficiently tune hyperparameters or optimize functions. Data Scientists often utilize Bayesian Optimization as part of their toolkit but have broader responsibilities, including data analysis, modeling, and reporting. While Bayesian Optimization focuses on optimization tasks, Data Scientists work on understanding and interpreting data to inform business decisions.
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Job description
Company Description
The Weather Company provides the best weather insight in the world, and is leading the charge in the growing area of weather decision support for business. We are offering you a unique opportunity to apply and/or develop your mathematical modeling skills on our unique set of weather data. Working with weather data is really unique and amazing; weather is in perpetual evolution, generates petabytes of new data every month, and deeply impacts people and businesses on various timescales. We serve a wide variety of businesses including renewable energies, energy traders, utility companies, insurance, retailers, and consumer product groups. As a consequence you will apply and/or learn a wide variety of statistical techniques including time series analysis, high dimensional clustering, machine learning, data mining and Bayesian modeling.
Job Description
Are you interested in applying machine learning or data mining on problems that truly improve people's life? We're looking for a mathematician/data scientist eager to tackle unique challenges in the realm of predicting weather's impact on business. You will work on a skilled team of passionate data scientists and meteorologists. Examples of projects you may encounter would be anything from predicting the electricity output of a solar park in Arizona, to predicting how much ice cream is going to be sold next week in Chicago.
Partner collaboratively with the business and project teams to accomplish tasks/milestones/goals.
Research, recommend, and implement statistical post process correction techniques using proprietary forecasts.
Demonstrate solutions by developing documentation, flowcharts, layouts, diagrams, charts, etc.
Improve operations by conducting systems analysis; recommending changes in policy and procedures.
Provide estimates of work effort and impact of projects and tasks, and provide team leadership, as required.
Continuously build your knowledge by studying new scientific methodologies and techniques.
Play an active role in the product requirements process, giving feedback to product management when challenges arise.
Qualifications
MS in Applied Statistics, Mathematics, Econometrics, or other discipline related to Time-Series Analysis, Machine learning and Forecasting, or other related discipline.
3-5 years of relevant professional experience, with demonstrated achievements.
Can demonstrate mastery of general scientific computing softwares such as R, MATLAB, Octave, etc.
Experience using/implementing non-parametric regression such as Neural Net, SVM, Random Forest, Projection Pursuit, MARS, Radial Basis Functions, AdaBoost, GLM
Experience in Predictive Modeling including Non-Parametric Regression, Bayesian Inference, Hidden Markov Models, Generalized ARMA, or Kalman Filtering is a plus.
Experience in non-linear optimisation including Simulated Annealing, Genetic Algorithm, Agent Based Modeling, Particle Swarm, Bee Colony is a plus but not necessary.
Knowledge of ensemble learning techniques and probabilistic forecasts is a plus.
Programming capabilities including C++, Java, Python is a plus but not necessary.
Additional Information