Data Scientist

About Fynite

Fynite enables business process automation through advanced AI/ML. Its cloud-native platform allows connection with 500+ data sources and seamlessly connects with your ERP system allowing instant end-to-end visibility, forecasting, and expert dialogues using conversational AI.

Advanced loss analysis tool utilizes in-memory databases, and distributed computing to handle large datasets and perform complex calculations in real-time, enabling it AI-powered brain to gain insights faster.

Our patent-pending technology accelerates the machine learning design process, enabling rapid implementation of use cases such as Dynamic Pricing and Risk Management - detecting issues before they occur and recommends the best course of action for efficient resolution.
 

Role Overview

We are looking for a seasoned Data Scientist who excels at translating complex business problems into measurable, data-driven solutions. The ideal candidate brings deep expertise in machine learning, statistical modeling, and end-to-end data pipeline development. You will work cross-functionally to design and deploy sophisticated models, leveraging your understanding of stochastic processes, systematic data modeling, and advanced feature engineering to uncover actionable insights and drive product and business impact.

Key Responsibilities

• Engage with stakeholders to understand business challenges and frame them into well-defined analytical or predictive modeling tasks.
• Explore and analyze complex datasets to identify patterns, trends, and opportunities for business optimization.
• Build and evaluate sophisticated machine learning models, including time-series models, probabilistic/stochastic models, and supervised/unsupervised learning systems.
• Design systematic data modeling workflows and pipelines to support scalable ML solutions and analytical insights.
• Lead the development of advanced feature extraction and transformation techniques to improve model performance and interpretability.
• Perform trend analysis and forecasting using a blend of statistical and algorithmic approaches.
• Develop and maintain robust, production-ready data pipelines and model inference systems in collaboration with engineering teams.
• Guide junior data scientists and contribute to establishing best practices for experimentation, validation, and deployment.
   

Requirements

• 2 years of experience in data science, machine learning, or applied statistics roles.
• Proven ability to convert ambiguous business needs into data-driven outcomes.
• Strong command of stochastic modeling, probability theory, and systematic data analysis approaches.
• Expertise in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and SQL; familiarity with distributed computing (Spark, Dask) is a plus.
• Experience with model deployment, pipeline orchestration (e.g., Airflow), and cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML Studio).
• Deep understanding of advanced machine learning techniques and evaluation methodologies.
• Strong communication skills with the ability to explain technical concepts to non-technical stakeholders.

Preferred Qualifications

• Advanced degree (Master’s or PhD) in Maths, Computer Science or related fields.
• Experience working with time-series, event stream, or sequential data modeling.
• Familiarity with CI/CD practices for ML and MLOps frameworks.
• Demonstrated success in leading data science initiatives from ideation to production.