Data Scientist, Advanced

Key Skills

AI technology, Algorithms, Business Intelligence, Data Architecture, Data Modelling, Deep Learning, Machine learning techniques, Mathematics, Python Programming, R Programming, Statistics

Job Description

Helps translate high level business problems into actionable and tangible Data Science projects. Analyzes, designs, develops, implements and maintains data science constructs, algorithms, programs, Statistical / Machine Learning Models, decision support system, Artificial/Augmented Intelligence systems using structured, unstructured, diverse big data sources of from measurement systems, IoT, human observations to provide real life business problem solutions

and product features/enhancements. Work is accomplished without considerable direction. Exerts significant latitude in determining objectives of assignment to create tangible impact in driving success factors and great experience for our customers and product users.

Responsibilities:

• Establishes requirements for highly complex data science constructs, statistical ML models, decision support systems and may lead a team for the same. Ensure correct constructs and most suitable models are incorporated in solutions, projects and products.
• Designs, Architects, Integrate highly complex data science projects. Completes all phases from data acquisition to incorporating into product/solutions. Follows through on all-in-process, final inspection and post production needs such as regular revision of the models / constructs for consistent fulfillment of business objectives.
• Drives reviews of the data science constructs, models and its implementation.
• Develops and applies customizations, improvements based on features data applicability and availability, constraints and consistent fulfillment of business objectives.
• Creates new data science constructs, models, algorithms, technologies, and solutions based on novel and unique ideas. Ensure that new ideas and methodologies are documented for use by Data Science and product engineering teams
•  Uses status as a knowledge leader in data sciences and functional knowledge across multiple related disciplines and program management to provide data science and decision support systems leadership to a given program.
• Defines requirements, data science constructs and may design complex new models and decision support systems to aid in analysis and solving on complex business problems.
• Initiates cross-company collaboration and develops positive relationships with other disciplines of engineering, system integration, professional services, solutions architects etc.
• Exercises judgement (with broadly defined policies and practices) in selecting methods, techniques and evaluation criteria for obtaining results. Make effective decisions for Build VS Buy by using industry standard auto ML models, Commoditized Statistical and relevant ML models, AI models and algorithms for optimized solution
• Makes use of Rapid Development Tools (Business Intelligence Tools, Graphics Libraries, Data modelling tools) to effectively communicate research findings using visual graphics, Data Models, ML model features, Feature engineering / transformations to relevant stakeholders
• May coordinate the activities of other personnel. Mentor, guide, leads and develop Data Scientists, Specialists within the team. Interacts with Cross-Functional teams to identify questions and issues for data engineering, ML models feature engineering.
• Network with key contacts outside own area of expertise, interacts with management and senior customer personnel on matters requiring coordination across organizational lines. Represent the organization as the principal customer contact and often performs project leadership. Interprets and communicates foresights, insights and findings from data science projects to product managers, service managers, business partners and business managers.
• Drive innovation by fostering open, high energy, collaborative environment; lead participation in innovation summit and expos, recommend relevant training and conference for employees to attend, publish papers and patent disclosures

Qualifications:

• B.Tech/M.Tech/PhD in CS/ML/Statistics
• Preferably 8+ years’ experience as Data Scientist OR in related field (Statistics / Operation Research) OR as System Architect / Enterprise Architect
• Design and conduct Analysis/Experiments (Plan Analysis and address competing explanations, Determine best way to evaluate results, Explore structured and unstructured data appropriately, Apply appropriate algorithms, Clearly document and articulate findings),
• Incorporate Analysis into pipelines and pipelines into business/Engineering stack (Read/write data to/from any format / location, incorporate complex matching and filtering, Make work compatible with/suitable for engineering stack, Discover needs of business, Navigate Business organization structure, package technical work for diverse audiences),
• Build the profession (Mentor/Coach Junior Data scientists, enable/upskill citizen data scientists)
• Problem Solving / Critical Thinking, Good business intuition,
• Programming and Coding (Python / R), Mathematics and Statistics (Equivalent to Graduate level Stat 101 and Math 101), Machine Learning / Deep Learning / AI, Communication / Appropriate articulation, Data Architecture, Risk Analysis / Systems Engineering