We are currently hiring Data Scientists Machine Learning at Fractal Sciences. Fractal Sciences is the Big Data management and technology arm at Fractal where we are building proprietary algorithms and analytics consumption platforms.
Fractal Sciences also collaborates with the analytics consulting teams to provide analytical solutions to complex business problems.
We are building a team of Data Scientists proficient in designing and developing solutions across domains applying machine learning and data mining techniques.
Lead analytics engagements guiding overall analytical sophistication, validating and improving methodology in predictive statistical modeling activities that deliver value to our clients
Develop and implement analytical/ML methodologies, processes and solutions that integrate diverse techniques, tools and technologies to generate deeper analytical insights
Guide Big Data engineers in implementing PoC solutions in production with due emphasis on scalability, validation, reliability and maintainability, and make appropriate design changes to accommodate these factors
Identify and create business opportunities for the development and redesign of data-driven products and solutions
Present and project the analytical capability of Fractal to help clients improve their business performance and solve challenging problems
Firm Building And People Management
Mentor teams to develop expertise in core ML techniques and create streamlined ML processes and workflows
Contribute to Fractal Academy of Analytics & other business-centric or firm-centric initiatives
Exhibit thought-leadership internally and externally by publishing academic and white papers, contributing to Fractals IP, participating in discussion forums and seminars
Exhibit Fractal values & contribute by building a positive work environment
Contribute to the hiring process by interviewing and structuring data science teams
Client Value Creation
Results orientation Should be able to translate business questions into analytical problems, build focused models, ensure solutions address the question, have tangible impact and are actionable
Drive service transformation Enhance productivity and efficiencies in data science teams, meet/exceed SLAs, enhance the delivery processes and deployment platforms
Collaborative Ability and willingness to effectively coordinate and communicate with diverse clients, internal teams across international time zones
Leadership (in thought and action) - Learn, adapt and lead new business and technical solutions. Assume ownership, require minimal supervision and consistently deliver excellence and motivates others
Passion for detail and accuracy with excellent written and oral communication skills
A strong theoretical and applied background in ML is essential
Strong mathematical background with grounding in theoretical frameworks: PAC, VC, Bayesian inferential, decision-theoretic
Deep expertise in core ML techniques: data-preprocessing and feature engineering; supervised (classification, regression, regularization for cross-section, time series and spatial data), semi-supervised and unsupervised (clustering, linear and nonlinear dimensionality reduction) learning methods, including adaptive and nonparametric methods; model performance metrics, loss functions and generalization error; ability to adapt models to class imbalance, outliers and other data issues
Expertise in at least one current research area in ML: reinforcement learning, deep learning, active learning, kernel methods, online learning, ensemble methods, learning to rank, graph algorithms, probabilistic graphical models, Gaussian processes and functional data analysis, approximate inference, natural language processing or sequential data mining
Familiarity with optimization and numerical computing methods
Demonstrable interest in large-scale learning, and distributed/parallel implementations of algorithms
Expert-level proficiency in at least one data analysis language: R, Python, Octave/Matlab.
Familiarity with lower-level languages such as C/C++ (preferred), Java, F#; the Linux environment and command-line tools; and SQL
Familiarity with tools in the Big Data stack, such as Hadoop (especially the MapReduce algorithmic framework), Pig/Hive, HBase/Cassandra; tools for large-scale learning such as Mahout, Pattern, Blaze, Neo4j, or Vowpal Wabbit
Domain expertise in one or more of the following
Financial services and insurance
CPG & retail
Healthcare and pharmaceuticals