3 Job openings found

About the Role : We are looking for a hands-on Full Stack Data Scientist who can independently manage the  entire machine learning lifecycle—from data wrangling to deployment—without relying on a  dedicated data engineering team. This role is ideal for someone who thrives in a fast-paced, self- directed environment and is passionate about building real-world ML solutions that drive  business outcomes.      Key Responsibilities : ∙Own the full ML pipeline: data ingestion, cleaning, feature engineering, model  development, deployment, and monitoring.  ∙Build and fine-tune models using Python and frameworks like Scikit-learn, XGBoost,  TensorFlow, or PyTorch.  ∙Deploy models using Databricks, MLflow, and cloud-native tools (preferably Azure).  ∙Develop robust, scalable pipelines using PySpark or native Databricks workflows.  ∙Collaborate with BI analysts and business stakeholders to translate requirements into  production-ready solutions.  ∙Maintain and improve existing models and pipelines with minimal supervision.      Required Skills : ∙3+ years of experience in applied data science or ML engineering.  ∙Strong Python programming skills, including experience with data manipulation and ML libraries.  ∙Experience with Databricks and cloud-based ML deployment (Azure preferred).  ∙Ability to work independently across the full stack of ML development and deployment.    ∙Familiarity with version control (Git), CI/CD, and MLOps best practices.  ∙Excellent communication skills and ability to work with remote teams across time zones.      Nice to Have  ∙Experience with data pipeline development using PySpark or Delta Lake.  ∙Exposure to Docker, REST APIs, or real-time inference.  ∙Prior experience working in a manufacturing or industrial analytics environment.    Interview Process:  Shortlisted candidates will be required to complete:  ∙An online technical skills assessment focused on Python and applied machine learning.  ∙An in-person practical test at our Ahmedabad Tech Center to evaluate real-world  problem-solving and deployment capabilities.  Hours: 2:30 PM – 11:30 PM IST (working from Office) Reports to: Manager, Data Analytics California, USA 

Roles & Responsibilities:    Developing trading strategies, from idea generation and data collection to analysis and model creation  Perform large-scale analysis on proprietary dataset to solve problems  Independent research on quantitative investment strategies across all asset classes  He / she shall be a part of a team that works in a lab environment designed to ...

Valuation Model Development: Designing, developing, and maintaining valuation models to assess the financial worth of assets, projects, or companies. This includes utilizing various valuation techniques such as discounted cash flow (DCF), comparable company analysis (CCA), and precedent transactions analysis (PTA). Financial Modeling: Creating and managing financial models to analyze and forecast ...