Quantitative Analytics Manager - AI and Machine Learning

Key Skills

Apache Hadoop, Apache Hive, Deep Learning, Java Programming, Apache Kafka, Keras software library, Nmap, PySpark, Python Programming, PyTorch, SQL, TensorFlow

Job Description

The Quantitative Analytics Manger role is responsible for building and managing a team of data scientists to design, develop, and implement AI models focused on SDI’s AI priorities including virtual assistant, predictive banking, and anomaly detection on our digital changes.In this role, you will:

• Manage a team responsible for the creation and implementation of low to moderate complex financial areas
• Mitigate operational risk and compute capital requirements
• Determine scope and prioritization of work in consultation with experienced management
• Participate in the development of strategy, policies, procedures, and organizational controls with model users, developers, validators and technology
• Make decisions and resolve issues regarding operational risks and enable decision making in business, product, marketing or other functional areas
• Manage a team comprised of quantitative analysts and credit risk analysts
• Interact with internal and external audit or regulators
• Manage allocation of people and financial resources for Quantitative Analytics
• Mentor and guide talent development of direct reports and assist in hiring talent

Required Qualifications, US:

• 4+ years of Quantitative Analytical experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 2+ years of leadership experience
• Master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, or computer science

Desired Qualifications:

• 4 years of management experience
• Strong analytical skills with high attention to detail and accuracy
• Ability to work and influence successfully within a matrix environment and build effective business partnerships with all levels of team members
• Meeting facilitation experience in leading discussions that result in consensus and commitment
• Managing or directing data scientist/ statistician/ data engineer teams
• Hands on familiarity with machine learning and statistical modeling techniques applying machine learning techniques such as neural networks, random forest, GBM and SVM, Probabilistic Graphical Models, Deep Learning architectures, using open-source languages like Python, PySpark, PyTorch
• Hands on experience with deep learning toolkits such as Tensorflow, Keras, PyTorch, Dynet
• Hands on experience writing data processing and data pipeline for model development including gathering and building datasets to collect intents, joining and aggregating source datasets, cleaning messy data, designing feedback loop on data need
• Detail oriented. Experience with model governance requirements. Able to de-mystify AI models to make them transparent and explainable
• Experience with agile project management methodologies for data science
• Experience with Big Data or Hadoop tools such as Spark, Hive, Kafka and Map

Job Expectations:

• Ability to travel up to 10% of the time