Data Scientist

1 month ago

Pune, India LoanTap Financial Technologies Full time

Company Description

LoanTap is an online platform in Pune committed to delivering flexible loan products to salaried professionals. The company focuses on innovation to provide smart products for millennials by leveraging technology to build flexibility in products at no additional cost.


Profile:

We are looking for a Data Scientist who will support our product, sales, leadership, and marketing teams with insights gained from analyzing company data. 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. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing AI models, using/creating algorithms, and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.


Role & Responsibility:

  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies
  • Analyze large datasets to extract actionable insights and support decision-making processes.
  • Collaborate with software engineers and product teams to integrate data science models into production environments.
  • Design and execute data-driven experiments, interpret results, and make data-backed recommendations.
  • Develop, implement, and optimize machine learning models to solve business problems.
  • Continuously monitor model performance and refine algorithms as necessary.
  • Present findings and recommendations to stakeholders in a clear and concise manner.
  • Develop company AI testing framework and test model quality.


Required Skills & Experience for this Role:

  • Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
  • Strong problem-solving skills with an emphasis on product development and creating data architecture.
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
  • Excellent written and verbal communication skills for coordinating and collaborating with all stakeholders.
  • We’re looking for someone with 3 - 5 years of experience manipulating data sets and building statistical models, has a  Computer Science or another quantitative field, and is familiar with the following software/tools:

·      Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.

·      Knowledge and experience in statistical and data mining techniques: GLM/Regression Random Forest, Boosting, Trees, text mining, social network analysis, etc.

·      Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.


Qualifications:

  • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field


Behaviour Skills & Competencies:

  • Analytical Thinking: Ability to approach problems logically and use data to identify patterns and solutions.
  • Problem-Solving: Develops creative and effective solutions to complex business problems using data-driven approaches.
  • Communication Skills: Clearly and effectively communicates complex data findings to non-technical stakeholders.
  • Collaboration and Teamwork: Works well with others to achieve common goals, contributing to a collaborative team environment.
  • Critical Thinking: Evaluates data and models critically, questioning assumptions and testing hypotheses to ensure robustness.


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