34 Job openings found

Key Responsibilities  Model Development & Training  ○ Design, train, and fine-tune deep learning and machine learning models (transformers, CNNs, RNNs, gradient boosting, etc.).  ○ Implement data preprocessing pipelines, feature extraction, and  augmentation techniques.  ○ Conduct hyperparameter optimization and experiment management. ● Evaluation & Optimization  ○ Run model evaluations, benchmark against baselines, and perform ablation studies.  ○ ...

Key Responsibilities: - 1. Model Development: Design, build, and optimize supervised, unsupervised, and deep learning models for various business problems. 2. Data Exploration & Feature Engineering: Clean, transform, and analyze structured and unstructured data; identify significant features. 3. AI Integration: Develop end-to-end AI pipelines and integrate models into production systems ...

Job description Role: MEP Modeler   Requirement: 2 candidates.Skill: Revit software, AutoCAD. LOD: UPTO 500.working Days - 5.5Days (2 alternate Saturday off - 2nd and 4th) working Days - 6 days  working time - 9:30 am to 6pm  working mode- work from office    Key Responsibilities: BIM Model Development: Develop detailed 3D BIM models of mechanical systems using industry-standard software such as ...

Credit Risk Analytics and Modelling – Analyse, model, validate and document various measures of Credit Risk for use in Expected Credit Loss and Capital computations. Hands-on experience in building, implementing, documenting, monitoring, validating, refining models and scorecards – in particular for PD, LGD, EAD and related Credit Risk metrics - using ...

Responsibility:   Powertrain vehicle function concept development based on function benchmarking. New vehicle function model development & validation. Final spec development after detail DFMEA & Design reviews.   Technical Competencies:   Perform requirement engineering and function development of function such powertrain vehicle function and CNG software in ECU. Perform function benchmarking suing actual vehicle and literature study. Prepare ...

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 

Required Skillset: 6-10 years of experience in Risk Management with consulting firms or Banks and other Financial Services Certifications like CFA, FRM, CQF Proficiency in MS Excel and PowerPoint Excellent knowledge of AI/ML techniques, including Python, R, and other relevant tools Strong communication skills (oral, written, and email drafting skills) Good organizational, analytical, problem-solving, and project ...

Responsibilities: ML model development. Graph Analytics. Data/Semantic modeling. Graph databases. Research and develop statistical learning models for data analysis. Collaborate with product management and engineering departments to understand company needs and devise possible solutions. Keep up-to-date with the latest technology trends. Communicate results and ideas to key decision makers. Implement new statistical or other mathematical methodologies as needed for ...

JD 1 : Quant Analyst – Market RiskRole Summary:The Market Risk Quant Analysts will focus on market risk model development, validation, andcompliance, ensuring that banks meet regulatory and risk management standards.Key Responsibilities: Validate market risk models, including Value-at-Risk (VaR), Expected Shortfall, and StressTesting frameworks. Develop and enhance models for FRTB ...

Responsibilities: Production pipeline development/deployment. DevOps tools. Automation/Orchestration. Release management. Model Testing. Environment Security. Performance Testing. Data governance. Design the data pipelines and engineering infrastructure Take offline models data scientists to build and turn them into a real machine learning production system. Identify and evaluate new technologies to improve the performance, maintainability, and reliability of our client's machine learning systems. Apply software engineering ...