- A P.hd in Statistical Analysis. Minimum 10+ years of experience in statistical calculations and analysis.
- Relevant experience in developing applications using SAS Enterprise Miner, IBM SPSS Modeler, R, Rapid Miner
- Experience in building models of different types - Regression, Classification, Clustering and Association, Time series
- Good understanding of business problems and application of analytics to the same
- Excellent data analysis and manipulation skills
- Experience in SQL and relational database programming
- Knowledge of SEMMA / CRISP-DM methodologies
- Adept at learning and applying new technologies
- Strong team player capable of working in a demanding research environment
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Innovative and strong analytical and algorithmic problem solvers.
- Proficiency with statistical analysis tools (e.g. R, SAS,SPSS)
- Proficiency with software development technologies (e.g. Python, Java)
- Extensive experience solving analytical problems using quantitative approaches (e.g. Bayesian Analysis, Reduced Dimensional Data Representations, and Multi-scale Feature Identification).
- Expert at data visualization, presentation and critical thinking skills,
- Experience with big data tools (e.g., Hadoop, HDFS, Cassandra, Storm)
- Establish scalable, efficient, automated processes for model development, model validation, model implementation and large scale data analysis.
- Develop metrics and prototypes that can be used to drive business decisions.
- Provide thought-leadership and dependable execution on diverse projects.
- Identify emergent trends and opportunities for future client growth and development
- Natural Language Processing: the interactions between computers and humans;
Machine learning : using computers to improve as well as develop algorithms;
Conceptual modelling : to be able to share and articulate modelling;
Statistical analysis : to understand and work around possible limitations in models;
Predictive modelling : most of the big data problems are towards being able to predict future outcomes;
Hypothesis testing : being able to develop hypothesis and test them with careful experiments.
- Excellent verbal and written communication skills
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