Sr Data Scientist

Qualification:

• Post-grad in one of the following fields with strong academic credentials:

• • Computer Science/IT.

• • Operations Research/Applied Math.

• • Engineering.

• • Statistics.

Responsibility:

Business:

• '•Works with the business team to identify the right business problem, gather the requirements and data required to answer the same.

• •Data exploration , hypothesis testing and statistical modeling are part of daily activities.

• •Involved in development , testing, evaluation and optimisation of models developed.

• •Analyzes data and generates insights that can articulated to business stakeholders.

• •Develops hypothesis for testing in consultation with Principal/Domain SME and Business teams.

Stakeholder Management:

• '• POC for all the daily based activities and ensures the availabilty of all the required information with all the team at all the times.

• '•Build the collaterals which are durable and reusable •Communicate analytical results in a way that is meaningful for business stakeholders and provides actionable insights.

• • Coordinates in communicating the data needs with both technology and business teams to ensure that right data is captured for analysis and modeling.

• 'Design qualitative & quantitative research instruments & methods (example: machine learning models, surveys, interviews etc) to capture the data if required.

• Integrate qualitative & quantitative information to create insights.

Project Management:

• '•Ensures that all the deliverables meets the delivery excellence standards and meets the stakeholders' expectations.

• • Identifies risks to project execution and works with stakeholders to mitigate the same.

• • Execute the design, analysis, or evaluation of assigned projects using sound engineering principles and adhering to business standards, practices, procedures, and product / program requirements.

Data Analytics and Reporting:

• '• Explore and examine data from multiple disparate sources.

• Prepare a data collection plan from both structured and unstructured sources.

• Collaborate and coordinate with Technology and Business teams for all data needs.

• • Expert level proficiency in data handling (SQL).

Data Discovery & Profiling:

• '• Perform exploratory data analysis and generate insights.

• Validate hypothesis developed during exploration phase.

• Present initial results to business stakeholders and identify the next steps.

• Design experiments with test and validate multiple hypothesis to meet/exceed expectations of customer due to the dynamic environment.

Data Modelling:

Create models using one or more of the platforms like R, SAS, Python, Matlab Model creation would involve one or more of the following technqiues:

• 1 Classification.

• 2 Clusterning, Segmentations.

• 3 Time Series.

• 4 Market Basket Anaysis.

• 5 Text Mining(Structured and Unstructured Data).

• 6 NLP, NLU, NLC.

• 7 Decision Trees, RF.

• 8 Network Analysis.

• 9 Linear Programming.

• 10 Optimisation.

• 11 Deep Learning.

• '• Testing and validating the model.

Salary and Other Compensation:For all remote positions and positions with a primary location within the states of California, Colorado, New York, and Washington, the remainder of this posting must be included:(Colorado and remote postings that could be performed in Colorado) . The annual salary for this position is between 120-140K depending on experience and other qualifications of the successful candidate.This position is also eligible for Cognizant’s discretionary annual incentive program and stock awards, based on performance and is subject to the terms of Cognizant’s applicable plans.Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:Medical/Dental/Vision/Life InsurancePaid holidays plus Paid Time Off401(k) plan and contributionsLong-term/Short-term DisabilityPaid Parental LeaveEmployee Stock Purchase PlanDisclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.

• • Deriving insights and recommendations from the models.

• • Performing data visualization and presentation to clients.

Innovation & Thought Leadership:

• '• Provide thoughtleadership and dependable execution on diverse projects.

• • implement best practices and technology.

• • Discover new avenues by disecting the data and identify which all models can be utilised for a given business problem.

• • Provide expertise thru PoCs and PoVs.

Knowledge Management:

• 'Prepare a design, requirement document.

• Document all modeling steps in a systematic way including modeling process, insights generated , presentations , model validation results and checklists built in the project.

• Prepare a one pager document that outlines and quantifies the business impact due to the DS project.

People/Team Management:

• ' Mentor a team of Data Scientists.

• Set the timelines and monitor the progress of the project.

• Ensure the timely delivery of deliverables and addresses the concerns related to tasks.

• Understand aspirations of team members.

• Set goals for team members and monitor performance.

• Conduct appraisals.

• Identify, priorities and deploy action items for competency development.

• Guide the employee in setting career paths.