Lead Data Scientist

Description
Duties and Responsibilities:
 Research and Develop Innovative Use Cases, Solutions and Quantitative Models
 Quantitative Models in Video and Image Recognition and Signal Processing for cloudbloom’s
cross-industry business (e.g., Retail, Energy, Industry, Mobility, Smart Life and
Entertainment).
 Design, Implement and Demonstrate Proof-of-Concept and Working Proto-types
 Provide R&D support to productize research prototypes.
 Explore emerging tools, techniques, and technologies, and work with academia for cutting-
edge solutions.
 Collaborate with cross-functional teams and eco-system partners for mutual business benefit.
 Team Management Skills
Academic Qualification
 7+ years of professional hands-on work experience in data science, statistical modelling, data
engineering, and predictive analytics assignments
 Mandatory Requirements: Bachelor’s degree with STEM background (Science, Technology,
Engineering and Management) with strong quantitative flavour
 Innovative and creative in data analysis, problem solving and presentation of solutions.
 Ability to establish effective cross-functional partnerships and relationships at all levels in a
highly collaborative environment
 Strong experience in handling multi-national client engagements
 Good verbal, writing & presentation skills
Core Expertise
 Excellent understanding of basics in mathematics and statistics (such as differential
equations, linear algebra, matrix, combinatorics, probability, Bayesian statistics, eigen
vectors, Markov models, Fourier analysis).
 Building data analytics models using Python, ML libraries, Jupyter/Anaconda and Knowledge
database query languages like  SQL
 Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM,
Decision Forests. 
 Strong Math Skills (Multivariable Calculus and Linear Algebra) - understanding the
fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis
of a lot of predictive performance or algorithm optimization techniques. 
 Deep learning : CNN, neural Network, RNN, tensorflow, pytorch, computervision,
 Large-scale data extraction/mining, data cleansing, diagnostics, preparation for Modeling
 Good applied statistical skills, including knowledge of statistical tests, distributions,
regression, maximum likelihood estimators, Multivariate techniques & predictive modeling
cluster analysis, discriminant analysis, CHAID, logistic & multiple regression analysis
 Experience with Data Visualization Tools like Tableau, Power BI, Qlik Sense that help to
visually encode data
 Excellent Communication Skills – it is incredibly important to describe findings to a technical
and non-technical audience
 Capability for continuous learning and knowledge acquisition.
 Mentor colleagues for growth and success
 Strong Software Engineering Background
 Hands-on experience with data science tools