Head HR at Lentra
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Lentra - Data Scientist (3-5 yrs)
Lentra relies on rich insightful data to power our credit risk models and scorecards. Credit risk analysis is one of the pillars and backbone of autonomous and presence less lending, helping financial institutions determine the creditworthiness of borrowers by quantifying the risk of loss that the lender is exposed to. We strongly believe in data-driven science, reinforced with artificial intelligence and machine learning to build the next-gen autonomous credit risk models/scorecards to help prevent fraud and defaults in the retail lending space.
Lentra's engineering team is seeking an experienced data scientist to deliver insights to us daily, having the potential to derive and extrapolate deep business insights from an ocean of structured and unstructured lending data. You should have the required mathematical and statistical expertise along with a naturally curious and creative mind. As you mine, interpret and clean our data, we will rely on you to ask questions, connect the dots, and uncover opportunities that lie hidden within. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. You must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms, and creating/running simulations. You must have a proven ability to drive business results with data-based insights. You must be comfortable working with a wide range of stakeholders and functional teams. You will join a team of data scientists and specialists, but will slice and dice data using your own methods, creating new visions for the future.
The ultimate goal is to realize the data's full potential by envisaging and implementing:
- Prediction (predict a value based on inputs)
- Classification (e.g. creditworthy or not)
- Recommendations (e.g. cross-selling of insurance, etc)
- Pattern detection and grouping (e.g. profiling - negative demography, fraud, dealer ranking, etc)
- Anomaly detection (e.g. fraud detection, out of geo, etc)
- Recognition (image, text, audio, video, facial, etc)
- Actionable insights (via dashboards, reports, visualizations, etc)
- Automated processes and decision-making (e.g. credit approval, superior straight through decisioning)
- Scoring and ranking (e.g. custom Lentra credit score)
- Segmentation (e.g. demographic-based marketing)
- Optimization (e.g. risk management)
- Forecasts (e.g. sales and revenue)
Objectives of this Role & Key Responsibilities:
- Identifying and integrating new datasets, understanding our product capabilities, and work closely with different teams to strategize and execute the development of data insights products.
- Execute analytical experiments methodically to help solve various problems and make a true impact across the lending domain. Research and devise innovative statistical and predictive models for credit risk analysis.
- Identify relevant data sources and sets to mine for client and product business needs, and collect large structured and unstructured datasets and variables.
- Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy.
- Analyse data for trends and interpret data/patterns with a clear objective in mind.
- Implement analytical models into production by collaborating with software developers and machine learning engineers.
- Build an overarching data insights platform aggregating all of Lentras AI-ML capabilities.
- Keep current with technical and industry developments.
- Build incremental and auto-learning processes to automate periodic training and accuracy assertions of existing credit risk and deep learning models.
- Engage customers (banks & FIs) and internal stakeholders, continuously working with them to improve Lentras offerings.
- Help Lentras sales team in promoting the platform across new and existing customers.
Skills and Qualifications:
- Bachelors degree in statistics, applied mathematics, computer science, or a related discipline.
- 3+ years experience in data science.
- Proficiency with data mining, mathematics, and statistical analysis.
- Advanced pattern recognition and predictive modeling experience.
- Excel, PowerPoint, Tableau.
- SQL and NoSQL databases.
- Linux OS (shell scripting, Unix commands, etc)
- Programming languages:
Python (primary language, strong hands-on experience)
Data architectures and pipelines.
Machine learning tools, libraries, and techniques:
- Python libraries and toolset:
- Supervised Learning:
- Decision Trees
- Unsupervised Learning:
- Pattern Detection
- Neural Networks
- Neural Networks (Computer Vision, NLP, etc):
- RNN, LSTM, GRU, etc.
Leveraging GPUs (NVIDIA Tesla V100) to run the various models.
Vertical and horizontal scaling of deployed models.
Performance/Latency tuning of built models.
- Knowledge and experience of ML offerings across cloud vendors: Azure, GCP, or AWS.
- Excellent written and verbal communication skills for coordinating across teams.
- Comfortable working in a dynamic, research-oriented group with several ongoing concurrent projects. A drive to learn and master new technologies and techniques.
- Masters degree or Ph.D. in statistics, applied mathematics, computer science, or a related discipline.
- Professional certifications.