Data Scientist - IT

- 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