Job Title: Data Scientist
Job Location: Yemen Kuwait Bank, Sana’a, Yemen
Application Deadline: 03 October 2022

Job Summary

Yemen Kuwait Bank (YKB) is a leading financial services firm, helping Yemeni's individuals and businesses achieve their financial goals through a broad range of financial products. YKB is reliant upon the confidentiality, integrity, and availability of its data and information to successfully conduct its operations, meet customers and staff expectations, and provide services. This is why at the heart of our digital transformation is data as well as assembling the right team of people in Data & Analytics domain.
As part of the transformation, we are looking for talented Data Scientist (but not only, see other job postings) to join our newly formed Data & Analytics unit. As a Data Scientist you’ll initiate and lead data science projects and use statistical methods such as hypothesis testing, factor analysis, regression analysis and clustering to unearth statistically sound insights. This includes defining the problem to be solved, writing queries to pull the right data from databases, cleaning and sorting the data, building and training machine learning models, and using data visualization techniques to effectively communication the findings to stakeholders.

Responsibilities

• Develop deep subject matter expertise across several banking products (cards, loans, deposits, investments, FX, etc.) and segments.
• Work with stakeholders to clearly define the problem they want to solve or question they need to answer, along with the project's objectives and solution requirements.
• Decide which analytic approach to follow, either 1) descriptive, 2) diagnostic 3) predictive, or 4) prescriptive; based on the business problem.
• Identify and acquire the data needed to achieve the desired result, perform data scrubbing this involves repairing, deleting, or normalizing data.
• Explore the data and applies statistical analytical techniques to reveal relationships between data features and the statistical relationships between them and the predicted label; using Python and Jupyter Notebooks.
• Build and train prescriptive or descriptive machine learning models, then test and evaluate the model to make sure it answers the question or addresses the business problem
• Deliver the final model with documentation and deploy the new dataset into production after testing, so it can play an active role in a business.
• Visualize and communicate the results in a way that makes it simple for non-technical audiences to understand -- using visualization tools like Microsoft Power BI, Apache Superset, and Metabase.
• Work with data engineer, data analyst and business analysts to assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Generate hypotheses and design experiments that deliver tangible business benefits.
• Collaborate with business teams to understand business needs and identify machine learning and artificial intelligence use cases across the bank.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Use a working knowledge of various AI capabilities such as computer vision, NLP, machine learning to build applications to solve real world problems.
• Work alongside data engineers and software developers to translate algorithms into commercially viable products and services.

Knowledge Areas

• Demonstrated skill in one or more financial services industry sectors
• Know PYTHON is more than a snake! You have programming skills needed for both the computational aspects of big data as well as working with statistical models (e.g., complex SQL scripts and with Pyspark / Jupyter Notebooks)
• Hands on experience of ML frameworks and toolkits (e.g. Tensorflow, PyTorch, Keras, scikit-learn etc.)
• Demonstrated skill in visualizing/presenting data for stakeholders using: Power BI, Apache Superset,D3, ggplot, matplotlib etc.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Some exposure to Deep Learning, Computer Vision or Reinforcement Learning is advantageous.
• Demonstrated skill in feature extraction and real-time analytics development and deployment
• Demonstrated skill in data and analytics cloud based tools (e.g. Apache Spark Azure Synapse, Azure Machine Learning, Databricks).
• Some Exposure to web development frameworks in Python (e.g. Flask, Django, Falcon, Hug etc.) is advantageous
• Knowledge of MLOps methods and practices.
• Awareness of DevOps and CI/CD principles.

Experience

• 5+ years of experience in a data-driven role
• Minimum of 3 years’ hands-on experience with multivariate analysis, statistical models and machine learning in financial services, customer analytics, or similar domains.
• Minimum of 3 years of hands-on experience in Big Data Programming such as Python, Scala-SPARK, Spark SQL.

Education

• Bachelor’s in Computer Science, Finance, Statistics or other relevant disciplines
• Master’s in Data Science or other quantitative fields is preferred

Certification

• Any Data Science Certifications is a plus
• Azure certifications will be added advantage (Azure Data Scientist Associate, Azure AI Engineer Associate)

Competencies

• Complex Problem-Solving (Level 5): Identifying complex problems and reviewing related information to develop and evaluate options and implement solutions. Using scientific rules and methods to solve problems.
• Service Orientation (Level 4): Actively looking for ways to help others as well as to make them feel attended to and welcome. The ideal candidate will have a passionate commitment to improving the lives of people, an insane focus on excellence and customer service, and a strong alignment with our core values.
• Critical Thinking and Analysis (Level 4): Using logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems as well as assessing performance of yourself, other individuals or organizations to make improvements or take corrective action.
• Programming (Level 4): Capacity to use programming to design machines or technological systems which fit user needs. That include writing computer programs for various purposes, especially in the big data space. You'll need expertise in at least one (or ideally all) of these languages (Python, PySpark, Scala, Java, R, SQL, or occasionally C++)
• Communication (Level 4): Communication is the ability to convey information in a way that others can understand. This includes conveying technical information in a way that non-technical employees can understand and vice versa. It also means communicating clearly about the positive effect their work has on business outcomes and help non-technical managers and executives understand what they do.
• Coding (code quality) (Level 4): Optimizes, challenges, follows the coding and documentation standards, and takes ownership of features by specifying, designing and writing code and documentation. Continues to partner with and performs code reviews for peers and others.
• Machine Learning APIs (Level 4): Interacting with Machine Learning APIs is an essential skill for any Data Scientist. As a data scientist, you may be required to convert your machine learning model into production-ready REST API that others can access.
• Business/Domain Expertise (Level 3): Is able to fully understand the bank's business process in the various domains and to design related products. As a data scientist you should be familiar with Financial Services data, would be expected to develop industry machine learning solutions such as fraud detection, risk modeling, and identifying ways to improve the customer experience.
• Analytical thinking and innovation (Level 3): Capacity to analyze information and use logic to address issues and problems, apply alternative thinking to develop new, original ideas and answers. 
• Attention to detail, trustworthiness (Level 4): Dependability, commitment to doing the job correctly and carefully, being trustworthy, accountable and paying attentive to details.
• Self-Development (Level 5): Actively seeks new ways to grow and be challenged using both formal and informal development channels. A lifelong learner and hold a growth mindset; we welcome individuals who geek out on industry podcasts, stay abreast of cutting edge techniques, or participate in KDD and Kaggle competitions for "fun" and love to share what they are learning with the rest of the team
• Collaborates (Level 5): Builds partnerships and works collaboratively with others to meet shared objectives. For example, encourages coworkers and external partners to work together as a team, and makes sure they get credit for doing so. Encourages people to share their honest views, responds in a non-defensive way when they do.
• English Language Proficiency (Level 4): Can understand the main ideas of complex text on both concrete and abstract topics, including technical discussions in his/her field of specialization. Can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party. Can produce clear, detailed text on a wide range of subjects and explain a viewpoint on a topical issue giving the advantages and disadvantages of various options.