Roles and Responsibilities

Data Science

- Hands-on Head of Data Science role to analyze massive sets of data, generate powerful insights, and create machine learning products to help our customers improve their topline and bottom-line performance

- Work hands on and lead the team on end-to-end: problem scoping, data gathering, EDA, modeling, insights, and visualizations

- Make sure the right modeling is being done with appropriate effectiveness, performance and reusability

- Spot opportunities to enhance customer delight by proactively solving customers- strategic problems, using a mix of Machine Learning products and advanced data science techniques that support executive decision making and call to action

- Actively support Sales and Marketing in responding to RFPs, supporting sales calls, delivering presentations, fielding questions from analysts, leading webinars, and traveling to customer locations to support sales meetings

- Accountable for identify incremental business opportunities through Data Science based, advanced analytical solutions focusing on entire customer life cycle across all businesses and generating relevant and actionable customer behavior insights that result in significant business impact.

- Maintain knowledge of current and emerging developments / trends in the Data Science space and collaborates with senior management to incorporate new ideas in current and future strategies

- Create IP for the organization and act as the driving force for filing patents, publishing whitepapers, blogs, research papers etc. Partner with academia

Data & Platform Engineering

- Set the vision, create a roadmap, and maintain (and invest) in infrastructure- team-process

- Oversee the development of the technology stack that will enable data exploration and analysis including data architecture, tagging and operational processes, data taxonomy, and reporting

- Ensure all the three phases of ETL (extract, transform, load) execute in parallel and are managed seamlessly

- Improve existing processes, identifying and taking steps to reduce technology gaps

- Manage reports, create dashboards, and visualize data to communicate the delivery of information to stakeholders

- Consider important KPIs and measurements including latency, concurrency, access pattern, queries, data scope, end users, and the technologies employed

Qualifications

- 15+ years of expertise working on and managing Machine Learning/Data Science teams with banks, enterprise or consumer-facing companies. At least last 5+ years- experience should be in managing Data Science team in Banks

- Ability to both manage and recruit a team while still being hands-on.

- Deep proficiency machine learning techniques and algorithm: k-NN, Naive Bayes, Bayesian models, SVM, Decision Forests, Random Forest, regression (logistics/linear), Decision Trees, tree-based learners, RF, XGBOOST, Time Series ARIMA/ARMA, instance-based learning, dimensionality reduction, SEM, GLM, GLMM, TSNE, PCA, clustering (K-means, Hierarchical and Self-organizing Maps), Neural Network, Deep Learning, ensemble methods and/or combinations etc.

- Fluency in R, Python, Pyspark or Julia

- Experience with relational databases / SQ & non-relational DB such as Dynamo, Cassandra, Hbase, or other

- High skill in data visualization. Familiarity with: Tableau, Looker, Qlikview, PowerBI, MSBI

- Comfort with ambiguity and constant change

- A strong communication skill set to make sure team understands the why behind what they are building as well as how they are going to measure to understand success

- Ability to travel at least 30-40%

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