Key Responsibilities:
1. Work within a strong data science department to analyze large datasets and identify patterns.
2. Define specific actions and measurements, and translate them into an operational context.
3. Apply machine learning and data mining techniques in areas such as statistical modeling, time series analysis, text mining, optimization, and information retrieval.
4. Utilize technical expertise in multivariate regression, Bayesian models, decision trees, random forests, support vector machines, ensemble models, boosting, bagging, bootstrapping, neural networks (recurrent and feedforward), deep learning, clustering algorithms, and cloud computing (Microsoft Azure).
5. Prototype statistical analysis and modeling algorithms and apply these algorithms to develop data-driven solutions for new domains.
6. Communicate and collaborate with clients to define analytics problems, formulate mathematical models, scope projects, select data sources, and establish performance metrics.
7. Develop analytics solutions aligned with business priorities by leveraging standard tools and domain expertise.

Qualifications:
1. A minimum of Master’s degree in Computer Science,
AI/ML, Statistics, Mathematics
2. Proven experience working in a strong data science department in an industrial environment.
3. Expertise in big data, pattern recognition, and operationalizing data insights.
4. In-depth knowledge of machine learning and data mining techniques.
5. Technical Expertise in multivariate Regression, Bayesian Models, Decision Trees, Random Forests, Support Vector Machines, Ensemble Models, Boosting, Bagging, Bootstrapping, Neural Networks (recurrent
and feedforward), Deep Learning, Clustering Algorithms, Cloud Computing
(Microsoft Azure), Map Reduce Ability to prototype statistical analysis and
modeling algorithms and apply these algorithms for data driven solutions to problems in new domains
6. Proficiency in English; additional languages are a plus.