Identify potential clients and generate leads through various channels Engage with clients to understand their preferences and recommend suitable deals Close sales and ensure customer satisfaction Maintain and grow relationships with clients to ensure repeat business Achieve and exceed sales targets on a monthly/quarterly basis Job Types: Full-time, Permanent Pay: ₹15,000.00 - ₹20,000.00 per month Compensation Package: Commission pay Performance bonus Experience: Real estate sales: 1 year (Required) Language: English (Required) Work Location: In person
Identify potential clients and generate leads through various channels Engage with clients to understand their preferences and recommend suitable deals Close sales and ensure customer satisfaction Maintain and grow relationships with clients to ensure repeat business Achieve and exceed sales targets on a monthly/quarterly basis Job Types: Full-time, Permanent Pay: ₹15,000.00 - ₹20,000.00 per month Compensation Package: Commission pay Performance bonus Experience: Real estate sales: 1 year (Required) Language: English (Required) Work Location: In person
Must have relatable skillset with PD LGD Validation , credit risk, python and SQL/SAS Location : Job role hiring across India(PAN India) Domain : Banking in a hybrid mode for Band B1 to C2 Candidate must apply if available immediate maximum up to 30 Days Notice period Salary that we are offering Up to 35 L Must have experience : 2-9 Years
Job Summary: We are looking for an Analytics Professional (48 years) with strong expertise in Credit Risk Strategy and Banking Analytics . The role combines credit risk policy analytics, portfolio strategy, and advanced data analytics to drive smarter decision-making in unsecured lending portfolios (Credit Cards, BNPL, POS Lending). The ideal candidate is hands-on with SQL and Python and has experience in credit risk analytics, policy development, portfolio tracking, and advanced data insights . This role requires working with senior stakeholders to influence risk strategy, optimize acquisition & collections policies, and deliver actionable insights through data. Key Responsibilities: Develop and enhance credit risk strategies for products such as Credit Cards, BNPL, and POS Lending . Conduct portfolio analytics, policy reviews, and performance tracking across acquisition, line management, collections, and fraud risk. Provide data-driven recommendations to optimize risk policies, funnel performance, and post-issuance loan behavior. Analyze large, complex datasets using SQL and Python to identify trends, patterns, and risk drivers. Build and automate dashboards & reports (Power BI / Tableau) to provide actionable insights to risk teams and senior leadership. Support strategic and tactical initiatives for US-based banks’ Risk Teams. Translate analytical findings into business-friendly presentations and risk strategies for technical and non-technical stakeholders. Partner with cross-functional teams in risk management, operations, and policy analytics to deliver impact. Mentor and guide junior analysts, supporting skill development in credit risk and analytics. Required Skills & Qualifications: Experience: 4–8 years in Analytics / Decision Science , preferably in BFSI (Banking & Financial Services). Technical Skills: Strong SQL & Python (mandatory) ; exposure to SAS, PySpark, ML techniques is a plus. Domain Expertise: Credit Risk Strategy, Portfolio Analytics, Policy Development, Risk Concepts (delinquencies, pre-payments, loss curves, collections, acquisition scorecards). Tools: Tableau/Power BI for dashboarding and reporting automation. Business Acumen: Strong understanding of Retail Banking / Lending / Risk Management .
Job Summary: We are hiring an experienced ML Modeler / Lead with strong hands-on expertise in developing, validating, and deploying machine learning models in the banking & financial services domain . The role focuses on credit risk models, acquisition & behavioral scorecards, collections strategies, and fraud detection models . The ideal candidate is a hardcore ML modeler who has built and implemented XGBoost, Decision Tree, and Ensemble models for BFSI, and can take end-to-end ownership from data preparation to deployment & monitoring . Key Responsibilities: Develop, implement, and validate ML models for credit risk, collections, behavior scorecards, and fraud detection . Build acquisition scorecards and customer lifecycle models for retail banking. Work on collections strategies, ECM, HRAM models to drive business impact. Hands-on development using Python, SQL, Scikit-learn, TensorFlow, PyTorch . Apply ML techniques (XGBoost, Decision Trees, GBM, Random Forest, Regression, etc.). Deploy models into production environments (Cloud / On-Prem) and monitor performance. Partner with model validation and MRM teams to defend models during audit. Lead/mentor junior data scientists and contribute to innovation in BFSI analytics . Desired Skills & Experience: 8+ years of applied ML model development in banking / financial services domain . Proven track record in credit risk modeling, collections, fraud detection, and behavioral scorecards . Strong Python, SQL, ML libraries (Pandas, Numpy, Scikit-learn, TensorFlow, PyTorch) . Hands-on with XGBoost, Decision Trees, Ensemble Methods . Experience with MLOps tools (MLflow, Airflow, Docker, Kubernetes) preferred. Excellent understanding of retail banking strategies underwriting, ECM, HRAM, recoveries .
Looking for a Data Scientist with strong SQL, Python & NLP expertise. Must excel in classical ML (XGBoost, regression, clustering, segmentation, RF, k-means), predictive/statistical modeling & consulting/analytics domain experience. Required Candidate profile Seeking Data Scientist with strong SQL, Python & NLP skills. Must excel in classical ML (regression, clustering, RF, XGBoost, k-means), predictive/statistical modeling, analytics domain experience.