Data Scientist

Role Summary:

In this role you will use statistical and machine learning techniques to support the operations and enable the growth of NeuralBank’s infrastructure. You will focus on long-term strategic initiatives that make NeuralBank’s infrastructure more scalable, efficient, and reliable. We are looking for hands-on experienced data scientists who are “full-stack” data scientists, to help establish product requirements, gather data, design experiments, create models, build software tools and communicate findings.

As a Data Scientist at NeuralBank, you will need to develop subject matter expertise, build trust with partners, recognize the biggest opportunities, create and drive strategy, and leverage data science methodologies to solve hard problems. In your work, you may provide guidance and coordinate with other data scientists to help achieve the goals in broad areas of operation.

Responsibilities:

• Identify how data science can be applied to improve, optimize, and expand NeuralBank’s infrastructure across a variety of domains, with emphasis on long-term and strategic initiatives.
• Work cross-functionally as a strategic partner to define priorities and develop project roadmaps in synergy with partner teams. Build consensus and earn commitment from partners. Drive execution through fast iteration.
• Ensure coordination of others projects across related workflows, to maximize impact and avoid duplication and overlaps.
• Employ languages and tools like Python, R, SQL, and others to drive efficient data exploration and modeling.
• Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging or developing statistical and machine learning methodologies.
• Generalize methodologies for broader application within and outside their domain.
• Lead and provide technical mentorship to data scientists, to ensure continuous up-leveling of our expertise.
• Work with large volumes of data; extract and manipulate large datasets using tools such as Spark SQL, command line and scripting languages.
• Analyze data covering a wide range of information from user profile to user behavior signals and identify new engagement patterns through data mining
• Conduct hypothesis driven exploratory analysis, build feature engineering, design the best structure, and select the most appropriate modeling techniques from a variety of machine learning models.
• Design, develop, validate and maintain big data-driven predictive models, tools and pipelines to improve user engagement using latest technologies in machine learning, user pattern recognition, and data modeling based on user lifecycle events.
• Research and prototype supervised and unsupervised machine learning solutions to a various tasks using both structured and unstructured data
• • Collaborate with product, engineering, business groups to translate a business question to a data science problem and formulate innovative solutions to experiment and implement sophisticated data mining techniques.
• Communicate complex concepts and the results of the analyses in a clear and effective manner to senior management.

Minimum Qualifications:

• Degree in quantitative field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research or other related field).
• 2+ years of experience doing quantitative analysis including experience with SQL, other programming languages (e.g, Python) or statistical/mathematical software (e.g, R, SAS, MATLAB).
• 2+ years of experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis.
• 2+ years experience developing production software systems such as data pipelines, deployed machine learning models, or dashboards.
• Experience initiating and driving projects to completion with minimal guidance.
• Proficient in writing code in Python in addition to one of the following languages Hadoop experience (MapReduce, Hive, Spark SQL), and SQL.
• Comfortable with large sets of data using cutting edge tools.
• Deep understanding of predictive modeling and algorithms for classification, regression, clustering, and anomaly detection.

Preferred skillset:

• Advanced degree (Master’s, PhD or equivalent experience) in quantitative field.
• 2+ years of experience doing complex quantitative analysis and working with distributed (i.e. Hive, Hadoop or similar databases) or highly complex datasets.
• 2+ years experience communicating complex research in a clear, precise, and actionable manner.
• 2+ years experience leading teams of other data scientists.
• Delivery-oriented, able to lead and execute modeling efforts from start to finish.

About Us:

NeuralBank is a data platform for ai applications in the Kingdom of Saudi Arabia. Data is the fuel that drives AI/ML development, NeuralBank supplies that fuel by offering clean and preprocessed high-quality labeled data. We fundamentally ensure that AI/ML and data science teams receive the accurate high-quality labeled data they need for their AI/ML applications. We also work on visualizing data, building data pipelines, and designing experiments to help businesses analyze and optimize their resources and set better strategic plans.

Our Mission:

Reliably supplying localized high-quality labeled data to accelerate the development of AI applications in the Kingdom.

Our Objective:

How would NeuralBank scale to a Unicorn? The responsibility of the core team is to support and enable the blitzscale of NeuralBank.