This Role will be responsible Testing Analytics , AI and Data implementations. Candiate will be responsible for 

• leading quality initiatives in AI products; prior experience in end-to-end quality process from data source to AI predictions
•  Load testing/performance testing using JMeter
• Build automation tools like Jenkins, Maven, GitHub, etc.
• Help tackle cross-platform engineering challenges and contribute creative ideas to improve quality.
• Define Quality Metrics and implement measurements to determine test effectiveness, testing efficiency, and measure the overall quality of the Product.
• Prior experience in mentoring a group of test engineers and pushing them to do better
• Strong verbal and written communication skills are essential as s/he collaborates with product managers, program managers, engineering managers and tech leads in building a quality-driven engineering culture that helps ship products rapidly and with good confidence.
• Prior experience in working on any cloud platforms like AWS, Azure is nice-to-have
• Experience with python scripting would be a plus.

The purpose of this role is to implement multiple BI & Data Analytics projects by working with key stakeholders specially Finance teams. The job holder will manage all future major/minor development enhancement and define architectural requirements in BI and Analytics.

The role requires job holder to be a creative thinker and able to propose innovative ways to look at business problems by using data mining (the process of discovering new patterns from large datasets) approaches on the set of Information available. They will need to validate their findings using an experimental and iterative approach. Also, Lead Data Scientist will need to be able to present back their findings to the business by exposing their assumptions and validation work in a way that can be easily understood by their business counterparts.

Lead Data Scientist will need a combination of business focus, strong analytical and problem solving skills and statistical programming knowledge to be able to quickly cycle hypothesis through the discovery phase of the data science projects. Excellent written and communications skills to report back the findings in a clear, structured manner are required. 

Finally lead data scientist will be primary owner for the implementation of the approved corporate Analytics strategy with an aim to develop actionable insights that continuously optimize business decisions.

Core responsibilities

• Apply Manual testing techniques to build, maintain, and improve on multiple decision systems, Designs experiments, and test business hypotheses.
• Identify what data is available and relevant in the bank for analytics opportunities, including internal and external data sources, leveraging new data collection processes such as geo-location data, Unstructured data sources such emails, log files, social media etc.
• Develop innovative and effective approaches to test the AI systems
• Utilize patterns and variations in the volume, speed and other characteristics of data supporting the initiative, the type of data (e.g. Images, text, clickstream or metering data) in predictive analysis.
• Provide on-going tracking and monitoring of performance of decision
• Recommend & implement ongoing improvements to methods and algorithms that lead to findings, including new information & patterns in the data.

People Management responsibilities

• Collaborate with team members and Business users across the Bank.
•  % Increase in organizational Analytics maturity score
• Number of areas/departments in the bank where advanced analytics solutions (Predictive & Prescriptive) were used for the first time.
• Business benefits realized through advanced analytics; depending on initiatives undertaken benefits may include
• % Increase in effectiveness in customer engagement (increased sales, reduced churn)
• % Decrease in customer acquisition costs
• % Decrease in sales costs
• % Increase in marketing effectiveness
• Number of identified organizational opportunities to improve data value
• Feedback from internal and external stakeholders.

Minimum Qualifications:

• Master’s in Business Administration from top B Schools. (preferred)
• Bachelor's degree in Statistics, Computer Sciences, Maths, Operations Research or other related fields.
• Additional degree or certifications in the field related to the data science/Analytics is preferred.

Minimum Experience:

• Minimum 1-2  years of experience in managing a large Analytics or BI function in a financial services organization.

Knowledge, Skills, and Attributes:

Knowledge and Skills

• Ability to independently manage analytics engagements from start to finish in testing, delivering actionable insights
• Develop  understanding of machine learning and Artificial Intelligence(AI) techniques and algorithms, such as k-NN, Naive Bayes, SVM, Random Forests, Deep Learning etc.
• The ability to come up with solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets.
• Use problem solving methodologies to propose creative solutions to solve business problem. Recommend design and develop state-of-the-art data driven analysis using statistical & advanced analytics methodologies to solve business problems.
• Strong communication skills with ability to expresses technical and business concepts, ideas, feelings, opinions, and conclusions verbally and in writing. Listens attentively and reinforces words through empathetic body language and tone.
• Experience leading teams.

Attributes

• Change Advocate: Identifies and acts upon opportunities for continuous improvement. Encourages prudent risk-taking, exploration of alternative approaches, and organizational learning.
• Conceptual & Innovating Thinking: Synthesizes facts, theories, trends, inferences, and key issues and/or themes in complex and variable situations. Recognizes abstract patterns and relationships between apparently unrelated entities or situations.
• Leadership: Leads members of formal and informal groups in the pursuit of common
•  Missions, vision, values and mutual goals. Places team needs and priorities above personal needs. Involves others in making decisions that affect them. Draws on the strengths of colleagues and gives credit to others' contributions and achievements