Data Scientist - NeuralBank

Role Summary: In this role you will use statistical and machine learning techniques to support the operations and enable the growth of NeuralBanks infrastructure. You will focus on longterm strategic initiatives that make NeuralBanks infrastructure more scalable efficient and reliable. We are looking for handson experienced data scientists who are fullstack 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 NeuralBanks infrastructure across a variety of domains with emphasis on longterm and strategic initiatives.
• Work crossfunctionally 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 largescale 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 upleveling 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 datadriven 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 (Masters 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.
• Deliveryoriented 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 highquality labeled data. We fundamentally ensure that AI/ML and data science teams receive the accurate highquality 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 highquality 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.

NeuralBank focuses on Machine Learning Big Data Artificial Intelligence Data Mining and Big Data Analytics. Their company has offices in Saudi Arabia and Riyadh. They have a small team thats between 110 employees.

You can view their website at confidential or find them on Twitter and LinkedIn.