Data Scientist

Data Scientist - GENAI

We are looking for a Data Scientist who will support building AI tools for customers. The candidate should be skilled at using large data sets to find opportunities for product and process optimization. The candidate must have a proven ability to drive business results with their data-based insights. The candidate must be comfortable working with a wide range of stakeholders and functional teams.

Primary Skills – Data Science, Python, AI, ML (Machine Learning) Gen AI – Generative AI

Secondary Skills – NLP, LLM, Deep Learning, Cloud (AWS/Cloud)

Responsibilities for Data Scientist:

- Work with stakeholders to identify opportunities for leveraging data to drive business solutions.
- Mine and analyse data from databases to drive optimization and improvement of product development and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom models and algorithms to apply to data sets.
- Use predictive modelling to increase and optimize customer experiences.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Analyse large amounts of information to discover trends and patterns.
- Build predictive models and machine-learning algorithms.
- Present information using data visualization techniques .
- Good to have a knowledge of ML lifecycle and model governance.

Qualifications for Data Scientist

- Looking for someone with 5-8 years of experience manipulating data sets and building statistical models, having bachelor’s or master’s degree in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools.
- Experience using statistical computer languages (R, Python etc.) to manage data and draw insights from large data sets.
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, XGBoost, KNN, SVM, ANN, etc.).
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
- 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.
- Experience visualizing/presenting data for stakeholders

- Experience with Snowflake will be an added advantage
- Experience in deployment of machine learning models using cloud technologies"