Data Scientist - R/Python (4-7 yrs)

Education Qualification : BE/ B. Tech, MCA, MSc (Statistics), MBA from Recognized University

Job Description :

We are looking for a Data Scientist to join our Data Science team. Data science drives all the products we develop here at Trellance. Our products are designed for small to mid-size financial institutions to help them create strategies based on data. The team is responsible for working on predictive model use cases and working closely with technical and functional stakeholders in an agile environment.

Role & Responsibilities :

- 3 - 6 years of relevant work experience in the Data Science/Analytics domain

- Work with the data scientists team, and data engineers.

- Take ownership of end-to-end data science projects, including problem formulation, data exploration, feature engineering, model development, validation, and deployment, ensuring high-quality deliverables that meet project objectives.

- Responsible for building analytic systems and predictive models as well as experimenting with new models and techniques.

- Collaborate with data architects and software engineers to enable deployment of sciences and technologies that will scale across the companys ecosystem.

- Responsible for the conception, planning, and prioritizing of data projects

- Provide support to inexperienced analysts with high-level expertise in an open-source language (e.g., R, Python, etc.)

- Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)

- Utilize data visualization tools (Power BI) to deliver insights to stakeholders.

Competencies and Technical Skills :

- Degree holder in computer science or related discipline

- Proficiency in SQL and database querying for data extraction and manipulation.

- Proficiency in programming languages such as Python/R, and experience with data manipulation and analysis libraries (e.g., NumPy, pandas, scikit-learn).

- Familiarity with data visualization tools (e.g., Tableau, Power BI) and proficiency in presenting complex data visually.

- Solid understanding of experimental design, A/B testing, and statistical hypothesis testing.

- Proven hands-on experience with machine learning algorithms and parameter tuning, including Ensemble methods (Random Forest, XGBoost), Logistic Regression, Support Vector Machines (SVM), and clustering techniques (e.g., K-Means, DBSCAN).

- Familiarity with Generative AI concepts and AI Agents is a plus.

- Excellent verbal and written communication skills, with the ability to effectively convey complex concepts to both technical and non-technical stakeholders

- Experience with Snowflake is not necessary but preferable.