7 Gbm Jobs

As a Quantitative Analytics Specialist at Wells Fargo, you will play a crucial role in independently overseeing the management of model risk exposures across the enterprise. Your responsibilities will include developing, implementing, and calibrating various analytical models, performing complex activities related to financial products and business analysis, and utilizing statistical and mathematical models using Python, R, SAS, C++, and SQL. You will also provide analytical support, offer insights on business initiatives, and analyze workflow processes to recommend improvements in risk management. To qualify for this role, you should have at least 2 years of Quantitative Analytics experience or equivalent demonstrated through work experience, training, military experience, or education. A Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or a quantitative discipline is required. Desired qualifications include experience in model validations for areas such as Treasury, Capital, Liquidity, Operational, and Business Planning, among others. You should be able to execute validation processes based on supervisory guidance, risk management policies, and industry best practices. Proficiency in Python, exposure to Balance sheet Models, and hands-on experience with quantitative methodologies will be beneficial. Additionally, you are expected to have practical quantitative programming experience with SAS, SQL, Python, and R, and be comfortable working with large datasets. Building and maintaining effective relationships with key partners and stakeholders, understanding model risk supervisory guidance, and continually striving to enhance the quality of independent model validation are also key aspects of this role. A PhD or Master's degree in relevant fields with 2+ years of experience is preferred. The shift timings for this role are from 1:30 PM to 10:30 PM. Candidates applying for this position are encouraged to support diversity initiatives. Wells Fargo values building strong customer relationships while maintaining a risk-mitigating and compliance-driven culture. Employees are responsible for executing risk programs and adhering to policies, procedures, and compliance obligations. Please note that the job posting may close early due to the volume of applicants. Wells Fargo is committed to maintaining a drug-free workplace and requires candidates to represent their own experiences during the recruitment and hiring process. If you have any disabilities and require accommodations during the application or interview process, please visit Disability Inclusion at Wells Fargo.,

6+ Years of prior analytics and data science experience in driving projects involving Advanced Analytics and Data Science Collaborate with cross-functional teams including Engineering and Business to experiment, implement and deploy complex AI and Gen AI capabilities Strong experience in Statistical Modelling and in Machine Learning techniques like Regression, GBM, Decision Trees, Random Forest, K-Means Clustering, Strong exposure in NLP/Text Analytics, SVM, LSTM, Deep Learning, Neural network Experience in deep learning frameworks such as Tensor Flow, Keras or PyTorch In-depth understanding and hands on experience in working with Large Language Models along with exposure in fine tuning open source models for variety of use case Strong exposure in prompt engineering, knowledge of vector database, langchain framework and data embeddings Strong problem-solving skills and the ability to iterate and experiment to optimize AI model behavior. Advanced proficiency in Python. Advanced experience in SQL/Hive will be preferred Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP) is a plus. Power BI/Tableau (preferred) Proven analytical and quantitative skills with an ability to use hard data and metrics to back up assumptions, develop business cases and complete root cause analyses Ability to communicate effectively with diverse clients/stakeholders Ability to manage a team Tier I/II candidates preferred

You are a highly skilled and experienced Machine Learning Modeling Lead being sought to join the ML Model Innovation team in the banking domain, particularly in retail transactional and lending areas. Your main responsibility will be to lead the development and deployment of machine learning models that drive crucial business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation, and personalized financial services. You are required to possess a strong understanding of banking business, data, and domain, covering the customer lifecycle as well as bureau/external data. Collaboration with cross-functional teams and providing technical leadership to other ML modelers and data scientists is a key part of the role. With a minimum of 8 years of experience in Retail banking, you should have deep business expertise in different business lines within the Credit risk space across the customer lifecycle. An in-depth knowledge of banking strategies related to Underwriting, Collection & Recoveries, High-Risk Account Management (HRAM), and ECM Strategies is essential. Additionally, familiarity with Fraud strategy and analytics in the Retail banking domain is advantageous. Your expertise should cover end-to-end hands-on ML solution development, including data exploration, feature engineering, model development, validation, deployment, and monitoring. Developing robust models to solve various business problems and drive business benefits is a core responsibility, along with supporting and reviewing junior data scientists" submissions and providing enhancement suggestions. Documentation, model reviews, submission reviews, managing queries from model validation teams, and defending models/solutions against internal MRM/audit teams are part of your duties. You will collaborate with implementation teams to deploy models into production environments, whether cloud or on-premise. Working closely with business stakeholders to translate banking domain challenges into data-driven solutions is crucial. Guiding junior data scientists and engineers on best practices in model development and MLOps, continuously evaluating new tools, technologies, and frameworks relevant to ML in finance, publishing internal research, and promoting a culture of innovation and experimentation are also key aspects of your role. Your educational background should include a Masters or similar degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field. You should have strong business knowledge of banking analytics across the retail banking customer lifecycle. With over 8 years of experience in applied machine learning model development in banking or financial services, hands-on experience leading ML projects and teams, expertise in model development, deployment, and monitoring in production environments, and familiarity with underwriting, collections, ECM, fraud, and ethical considerations in banking ML models, you are well-equipped for this role. In terms of skills, you should be an expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch), and techniques such as Regression, Decision Trees, Ensembles (XGBoost, GBM, Random Forest, Unsupervised Learning, etc.). Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is a plus. A strong grasp of statistical modeling, optimization, and deep learning techniques, along with excellent communication skills and the ability to explain complex concepts to non-technical stakeholders, are essential for success in this position.,

Company Description WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees. Job Description Job Overview: Responsible for leading data science initiatives, developing advanced analytics models and ensuring successful execution of data-driven projects for clients in the retail. Will work closely with key client stakeholders to understand their business challenges and leverage data science to deliver actionable insights that drive business growth and efficiency.Lead the design, development and implementation of advanced analytics models. Including predictive and prescriptive models for retail clients.Should be able to convert mathematical/ statistics-based research into sustainable data science solutionsCandidate should be able to think from first principles to define & evangelize solutions for any client business problemLeverage deep knowledge of the retail to develop data-driven solutions that address industry-specific challenges. Apply AI/ML statistical methods to solve complex business problems and determine new opportunities for clients.Ensure project delivery of high-quality, actionable insights that drive business decisions and outcomes.Ensure end-to-end lifecycle (scoping to Delivery) of data science projects.Collaborate with cross-functional teams to ensure seamless project execution.Manage timelines, resources, and deliverables to meet client expectations and project goals.Drive innovation by exploring new data science techniques, tools, and technologies that can enhance the value delivered to clients.Strong hands-on experience with data science tools and technologies (e.g., Python, R, SQL, machine learning frameworks).Hand-on experience with a range of data science models including regression, classification, clustering, decision tree, random forest, support vector machine, nave Bayes, GBM, XGBoost, multiple linear regression, logistic regression, and ARIMA/ARIMAX.Should be competent in Python (Pandas, NumPy, scikit-learn etc.), possess high levels of analytical skills and have experience in the creation and/or evaluation of predictive models.Preferred experience in areas such as time series analysis, market mix modelling, attribution modelling, churn modelling, market basket analysis, etcGood communication and project management skills. Should be able to communicate effectively to a wide range of audiences, both technical and business. Adept in creating Presentations, reports etc to present theanalysis findings to key client stakeholders. Strong team management skills with a passion for mentoring and developing talent. Qualifications BTech/Masters in Statistics/Mathematics/Economics/Econometrics from Tier 1-2 institutions Or BE/B-Tech, MCA or MBA7-10 years of hands-on relevant experience in delivering Data Science/Analytics projects. Show more Show less

Job Title: Assistant Manager Immigration (Work Permit Processing) Location: Gurgaon Work Mode: Hybrid Experience Required: 6+ years Industry Preference: IT / ITES Role Overview We are seeking a seasoned professional with deep expertise in end-to-end work permit processing across geographies including the U.S., U.K., Canada, Poland, Australia, Singapore and others. This role demands hands-on knowledge of government filings, documentation management , and process coordination specifically related to employment-based migration. Individuals from the IT/ITES domain with relevant exposure to immigration compliance will thrive in this position. Key Responsibilities Manage and oversee full-cycle work permit filings and documentation for visa categories: United States: L1A, L1B, H1B from petition initiation to attorney engagement Canada: ICT, LMIA– handling documentation and labor market formalities United Kingdom: GBM, SWB– with emphasis on CoS certification protocols Additional focus on Poland, Australia, and other countries Coordinate with government agencies and authorized consultants to ensure timely and compliant submissions Own the lifecycle of work authorization processing including document collation, escalation handling, and audit preparedness Collaborate with internal stakeholders and HR teams on immigration strategy, compliance tracking , and data accuracy Monitor and interpret regulatory updates related to employment-based immigration for supported geographies Leverage past experience in employment migration, not stamping/business visa. Drive in-house petition filing and ensure coordination with legal counsel and authorized consultants Review and verify petition documents to align with immigration and employment regulations. Maintain compliance through proactive tracking of SLA adherence, audit preparedness, and document integrity Partner with internal HR teams and external consultants for immigration strategy alignment Interpret and apply regulatory updates impacting e mployment-based visas and work authorizations Candidate Profile Minimum 6 years of core experience in work permit processing Strong understanding of filing protocols with government bodies , labor market assessments, and visa issuance norms Familiarity with compliance audits, SLA tracking , and vendor coordination Excellent communication and documentation skills, with a proactive problem-solving approach Not What We're Looking For Experience limited to business visa processing or visa stamping workflows Candidates without hands-on filing or coordination experience with immigration authorities If interested share your cv at aishwarya@beanhr.com

Responsible for developing data-driven innovative scalable analytical solutions and identifying opportunities to support business and client needs in a quantitative manner and facilitate informed recommendations / decisions. Accountable for delivering high quality project solutions and tools within agreed upon timelines and budget parameters and conducting post- implementation reviews. Contributes to the development of custom analyses and solutions, derives insights from extracted data to solve critical business questions. Activities include developing and creating predictive models, behavioural segmentation frameworks, profitability analyses, ad hoc reporting, and data visualizations. Able to develop AI/ML capabilities, as needed on large volumes of data to support analytics and reporting needs across products, markets and services. Able to build end to end reusable, multi-purpose AI models to drive automated insights and recommendations. Leverage open and closed source technologies to solve business problems. Work closely with global & regional teams to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support analytics and reporting needs across products, markets, and services. Support initiatives in developing predictive models, behavioural segmentation frameworks, profitability analyses, ad hoc reporting, and data visualizations. Translates client/ stakeholder needs into technical analyses and/or custom solutions in collaboration with internal and external partners, derive insights and present findings and outcomes to clients/stakeholders to solve critical business questions. Create repeatable processes to support development of modelling and reporting Delegate and reviews work for junior level colleagues to ensure downstream applications and tools are not compromised or delayed. Serves as a mentor for junior-level colleagues, and develops talent via ongoing technical training, peer review etc. All About You 6-8 years of experience in data management, data mining, data analytics, data reporting, data product development and quantitative analysis. Advanced SQL skills, ability to write optimized queries for large data sets. Experience on Platforms/Environments: Cloudera Hadoop, Big data technology stack, SQL Server, Microsoft BI Stack, Cloud, Snowflake, and other relevant technologies. Data visualization tools (Tableau, Domo, and/or Power BI/similar tools) experience is a plus Experience with data validation, quality control and cleansing processes to new and existing data sources. Experience on Classical and Deep Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks - Feedforward, CNN, NLP, etc. Experience on Deep Learning algorithm techniques, open-source tools and technologies, statistical tools, and programming environments such as Python, R, and Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning. Experience in automating and creating data pipeline via tools such as Alteryx, SSIS. Nifi is a plus Financial Institution or a Payments experience a plus Additional Competencies Excellent English, quantitative, technical, and communication (oral/written) skills. Ownership of end-to-end Project Delivery/Risk Mitigation Virtual team management and manage stakeholders by influence Analytical/Problem Solving Able to prioritize and perform multiple tasks simultaneously Able to work across varying time zone. Strong attention to detail and quality Creativity/Innovation Self-motivated, operates with a sense of urgency. In depth technical knowledge, drive, and ability to learn new technologies. Must be able to interact with management, internal stakeholders

Job Title: Data Scientist Location: Bangalore, India Experience: 3+ Years Role & Responsibilities: We are looking for a highly motivated Data Scientist to join our analytics team in Bangalore. This role is ideal for individuals who possess strong machine learning and data engineering skills and are eager to solve complex problems in the financial technology space. You will work on developing predictive models, building scalable data solutions, and contributing to key decision-making processes. Design, build, and validate machine learning models including Logistic Regression, Random Forests, GBM, and Survival Models. Perform rigorous model evaluation using techniques such as K-Fold Cross Validation, Out-of-Time (OOT) validation, and X-Tab analysis. Utilize Python (via PyCharm/Jupiter), scikit-learn, PyTorch or TensorFlow for end-to-end model development. Analyse large datasets using SQL and Excel to derive actionable insights. Develop and deploy APIs to serve machine learning models in production environments. Apply statistical techniques such as mean/variance analysis, probability distributions, and simulations to drive model accuracy and relevance. (Optional) Work with lending-specific metrics such as vintage curves, roll forward tracking, and bounce rates to enhance financial models. Strong ML skills: Logistic, Random Forest, GBM, survival model, K fold test, OOT, X-Tab etc. Experience using scikit-learn, pytorch or tensor flow. Experience: 3+ Years Python and usage of PyCharm/ Jupiter, SQL, Excel. Well versed in API development - Strong mathematical ability. mean, variance, probability distributions, simulation etc. Understanding of lending metrics such as vintage curve, roll forward, bounces etc. (Good to have not mandatory) Engineering background preferred IIT/NIT plus not must. Prior experience in Lending Fintech preferred.