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
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
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
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.