Data Scientist

GROUP FUNCTION AND ROLE PURPOSE
Data Scientist role is responsible for modelling complex Institutional problems discovering business insights and identifying opportunities through the use of statistical algorithmic mining and visualization techniques. In addition to advanced analytic skills, this Job holder is also proficient at integrating and preparing large varied datasets, architecting specialized database and computing environments, and communicating results. Lead data scientists work closely with Business leaders, Business champions, data stewards, project/program managers, and IT teams to turn data into critical information and knowledge that can be used to make sound organizational decisions for First Abu Dhabi Bank.
The role requires the 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, 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.
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, the data scientist will be the primary owner for the implementation of the approved corporate Analytics strategy with an aim to develop actionable insights that continuously optimize business decisions.

Key Accountabilities:

1. Strategic responsibilities
   Develop & conceptualize sophisticated & innovative analytical solutions that integrate diverse information assets available at First Abu Dhabi Bank to achieve incremental revenue or reduction in costs or mitigating financial risks.
2. Lead the discovery process for the analytics opportunities with the institutional stakeholders to identify the business requirements and the expected outcome.
3. Make strategic recommendations on data collection, integration, and retention requirements incorporating business requirements and knowledge of best practices.
4. Define standard metrics and KPIs for measuring the success of all analytics engagements undertaken and establish processes to measure them.
5. Educate the organizational colleagues from both IT and the business perspectives on new approaches such as testing hypotheses and statistical validation of analytical results. Helps the organization understand the principles and the math behind these statistical processes to drive the organizational adoption.

1. Core responsibilities
   Apply advanced statistical and predictive modelling techniques to build, maintain, and improve on multiple decision systems. Design experiments and test business hypotheses.
2. 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 geolocation data, unstructured data sources such as emails, log files, social media, etc.
3. Develop innovative and effective approaches to solve business problems using Analytics and communicate results and methodologies.
4. 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.
5. Provide ongoing tracking and monitoring of performance of decision systems and statistical models built by the data science unit.
6. Recommend & implement ongoing improvements to methods and algorithms that lead to findings including new information & patterns in the data.
7. Establish conducive ecosystem for analytics using tools, processes, vendors, etc. within the bank that will enable adoption of analytics across the organization.
8. Play the role of bank wide machine learning and Artificial Intelligence expert to use these techniques to solve the real business problems.

1. People Management responsibilities
   Coach & groom the data scientists in the team on gaining knowledge & skills on the principles of analytics techniques, problem solving, project management, client relationship management & team work skills. Conduct regular reviews with team members.
2. Responsible to hire, train, motivate, and continuously develop skillsets of the data scientists in the team.
3. Define goals and key performance indicators for each member of data science team and coordinate the performance management review process for the team.

Requirements

TECHNICAL SKILLS: MINIMUM 4 YRS OF WORKING EXPERIENCE MANDATORY
SAS (Base, SASEMiner, EG), Python, Statistical modelling, Machine Learning and AI (Pattern recognition, Natural language processing, computer vision), Data Visualization (Power BI), Unstructured data (SAS text analyzer), Data modelling: json, xml, Deep learning technologies (Neural networks, LSTM, GAN, RNN, etc.), Deep learning frameworks (Tensorflow/PyTorch/Maxnet), Cloud deployment: Microsoft Azure / AWS, Exposure to distributed computing environments / big data platforms (Cloudera, Hadoop, Apache Spark, Elastic search, etc.) as well as common database systems and value stores (SQL, Hive, HBase, etc.)

KEY PERFORMANCE INDICATORS (KPIS):

1. Business benefits realized through advanced analytics; depending on initiatives undertaken benefits may include:
2. Increase in effectiveness in customer engagement (increased sales, reduced churn)
3. Decrease in customer acquisition costs
4. Decrease in sales costs
5. Increase in marketing effectiveness
6. Number of identified organizational opportunities to improve data value
7. Increase in organizational Analytics maturity score
8. Feedback from internal and external stakeholders

Benefits

KNOWLEDGE, SKILLS & EXPERIENCE:

Minimum Qualifications:
Masters in Business Administration from top B Schools.
Bachelors degree in Statistics, Computer Sciences, Maths, Operations Research or other related fields.
Additional degree or certifications in the field related to data science/Analytics is preferred.

Minimum Experience:
Minimum 4 years of experience in Data Science (Analytics, BI, ML Modeling, Deep learning Algorithms and ML Ops) in BFSI Sector.
Typically 2 years of relevant quantitative and qualitative research and analytics experience.

Knowledge Skills and Attributes:

Knowledge and Skills
Ability to independently manage analytics engagements from start to finish delivering actionable insights within established timelines and budget.
Excellent understanding of machine learning and Artificial Intelligence (AI) techniques and algorithms such as kNN, Naive Bayes, SVM, Random Forests, Deep Learning, etc.
Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing and optimization algorithms.
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 problems. Recommend, design, and develop state-of-the-art data-driven analysis using statistical & advanced analytics methodologies to solve business problems.
Strong programming skills and hands-on experience on statistical modelling packages (like SAS, R, or Python).
Experience in building a multigenerational scalable platform.
Strong communication skills with the ability to express 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.
In-depth industry/business knowledge.

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.