iv. Lead development of product marketing plan, proof of concept and business case for key offerings to enable solution led sales
b. Educate sales teams, BU client servicing team and client teams about advance analytics practice's offerings and differentiation
c. Drive building of sales pipeline through upstream Consulting services
i. Build Advisory and Consulting capabilities in identified technical and functional domain areas for niche differentiation
ii. Grow consulting led sales to develop higher value revenue streams
d. Develop and demonstrate thought Leadership to showcase Wipro's capability in AI/ ML and data science solutions
i. Manage relationships with Analysts and Industry groups in order to proactively shape market's perception of Wipro's strength in this domain
ii. Formalize and drive targets for analyst rankings, client testimonials and partner credits to attain market referencability and recognition
iii. Represent Wipro in industry forums in the practice domain to gain and retain high mindshare of key vendor partners
iv. Be the voice of Wipro's Thought Leadership in the Practice domain by speaking in industry forums, seminars, writing blogs, articles, whitepapers etc.
e. Partner with BUs and alliance partners to conduct events, develop presentations or other materials to present thought leadership in client forums
f. Collaborate with different colleges and institutes for recruitment, research sponsorship, joint research initiatives and provide data science courses

2. Optimize business growth by providing apt solution to the clients
a. Guide and inspire the organization about the business potential and strategy of artificial intelligence (AI)/data science
b. Identify data-driven/ML business opportunities across the BU and drive
c. Develop business understanding by aligning the technical strategy of the SL with Wipro's strategic priorities and goals & thereby providing best possible solution to the client
d. Acquire new data sources and process pipelines and catalog, document them for more efficient and repeatable data science projects
e. Collaborate with BU sales, pre-sales, delivery leadership teams and domain experts to better understand the business mechanics that generates data and what relevant solutions can be offered to a specific business problem of a client
f. Lead, monitor and review technology projects across the SL for developing capabilities, solutions and services
g. Collaborate with Holmes engineering team to identify different initiatives for joint solutioning approach
h. Problem analysis and project management
i. Apply statistical analysis and visualization techniques to various data to provide best solution to the business problem through "feature engineering"
ii. Generate hypotheses about the underlying mechanics of the business process and testing it using various quantitative methods
iii. To code various scripting languages: SQL, Perl, Python, etc., to cleanse raw data and bring it into a usable form
iv. Appropriately vectorize (transform, convert and model) text, image, audio, machine/consumer data, time-series data and potentially geolocation or log data
v. Perform data sampling and data aggregation, fuzzy encoding, etc
vi. Leverage existing data science solutions/ products and provide services to a client

3. Build, test & operationalize Machine Learning Models
a. Integrate various machine learning models - advanced regression, clustering & ensemble techniques and time-series analysis to perform classification tasks
b. Solve a variety of business contexts: for example, financial risk, customer journey modeling, quality management, sales and marketing
c. Real-world testing of ML models such as champion-challenger (A/B testing) and cross validation
d. Integrate domain knowledge into the Machine Learning (ML) solution and testing the same
e. Collaborate with ML operations ,data engineers and IT to evaluate and implement ML deployment options
f. Integrate model performance management tools into the current business infrastructure
g. Implement challenger tests on production systems and continuously monitor execution and health of production ML models
h. Establish best practices around ML production infrastructure

4. Team Management
a. Resourcing
i. Forecast talent requirements as per the current and future business needs
ii. Hire adequate and right resources for the team
b. Talent Management
i. Ensure adequate on boarding and training for the team members to enhance capability &effectiveness
ii. Build an internal talent pool and ensure their career progression within the organization
iii. Manage team attrition
iv. Drive diversity in leadership positions
c. Performance Management
i. Set goals for the team, conduct timely performance reviews and provide constructive feedback to own direct reports
ii. Ensure that the Performance Nxt is followed for the entire team
d. Employee Satisfaction and Engagement
i. Lead and drive engagement initiatives for the team
ii. Track team satisfaction scores and identify initiatives to build engagement within the team

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