Assistant Manager - Analytics\..RBG - Analytics
Mashreq
IndiaremotePosted 10 days ago
Skill Required
Analytics-ManagerMachine-Learning-EngineerAI-EngineerData-ScientistDigital-Analytics-SpecialistAssistant-ManageranalyticsGenerative AIMachine LearningGoogle Analyticsdata engineeringScikit-learnEngineeringR ProgrammingTensorFlowStatisticsanalyticalautomationdevelopingData StructuresLangChainPower BIXGBoostPyTorchTableauSparkPythonPandasdesignOpenAPINumPyFulltime
Key highlights
- Requires extensive banking domain experience across SME, commercial, corporate, and identity portfolios.
- Needs strong analytical and problem‑solving abilities.
- Must be proficient in Python and SQL for data extraction and analysis.
- Should have foundational knowledge of machine learning, statistics, and AI engineering concepts.
- Possesses experience with Large Language Models (LLMs) and their applications in banking.
- Holds a bachelor’s or master’s degree in a relevant quantitative discipline (Computer Science, Data Science, AI, Statistics, Engineering, or related field).
Role overview
The Assistant Manager – AI & Machine Learning leads the development of advanced analytics and AI‑driven solutions for digital banking. The role focuses on turning customer digital footprints into predictive models, optimizing marketing campaigns, and improving operational efficiency through AI and Large Language Model (LLM) technologies. The manager collaborates with digital banking, marketing, and product teams to deliver data‑driven insights and AI‑enabled products that enhance performance, engagement, and customer experience.
Responsibilities
- Develop and deploy machine learning models for customer segmentation, churn prediction, next‑best‑product recommendations, and campaign targeting.
- Implement predictive analytics models to improve customer engagement and product adoption across digital channels.
- Continuously monitor model performance and refine algorithms to increase accuracy and business impact.
- Support marketing teams in evaluating digital campaign performance using advanced analytics and AI‑driven insights.
- Build models for campaign targeting, customer propensity, and marketing attribution.
- Provide insights on channel effectiveness, campaign ROI, and customer acquisition strategies.
- Support design and development of AI‑powered solutions using Large Language Models (LLMs) for digital banking use cases.
- Integrate AI models and APIs into banking platforms and analytics workflows.
- Experiment with prompt engineering and model fine‑tuning to enhance AI solution performance.
- Extract, clean, and transform large datasets from multiple banking systems and digital platforms.
- Develop feature engineering strategies to improve machine learning model performance.
- Work with data engineering teams to ensure efficient data pipelines for analytics use cases.
- Collaborate closely with digital banking, marketing, and product teams to identify data‑driven opportunities.
- Translate business requirements into analytical models and actionable insights.
- Present findings and recommendations to stakeholders to support strategic decisions.
- Analyze complex datasets to identify patterns, anomalies, and opportunities that improve customer engagement and digital banking performance.
- Apply statistical techniques and machine learning methods to solve real business challenges.
- Support decision‑making related to the selection and application of AI/ML models.
- Evaluate trade‑offs between model performance, scalability, and usability in production environments.
- Translate complex analytical outputs into clear business insights for marketing strategies and product development.
- Work with large and complex datasets.
- Understand digital customer journeys and online behavior analytics.
- Possess a foundational understanding of machine learning, statistics, and AI engineering concepts and LLM applications.
- Communicate effectively and present insights to non‑technical audiences.
- Adopt a collaborative approach when working with cross‑functional teams.
- Hold a bachelor’s or master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or a related quantitative field.
Requirements
- Bachelor’s or master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or a related quantitative field.
- Proficiency in Python for machine learning and data analysis.
- Experience with SQL for data extraction and manipulation.
- Strong background in machine learning algorithms (regression, classification, clustering, recommendation systems).
- Hands‑on experience with ML libraries such as Scikit‑learn, TensorFlow, PyTorch, XGBoost, or LightGBM.
- Familiarity with Large Language Models (LLMs) and their applications in banking.
- Experience with LLM APIs and frameworks (e.g., OpenAI, LangChain).
- Basic knowledge of prompt engineering, embeddings, and retrieval‑augmented generation (RAG).
- Familiarity with building AI‑enabled applications such as chatbots or knowledge assistants.
- Experience handling large datasets using Pandas, NumPy, and big data frameworks (PySpark or Spark).
- Proficiency with digital analytics tools (Google Analytics, Adobe Analytics, etc.).
- Ability to create dashboards and visualizations using Power BI, Tableau, or similar BI tools.
- Strong analytical and problem‑solving abilities.
- Capacity to work with large and complex datasets.
- Understanding of digital customer journeys and online behavior analytics.
- Foundational knowledge of machine learning, statistics, AI engineering concepts, and LLM applications.
- Effective communication and presentation skills.
- Commitment to collaboration with cross‑functional teams.
Nice to have
- Knowledge of R (an added advantage)
- Specific experience with LLM API frameworks such as OpenAI or LangChain
- Advanced prompt engineering and model fine‑tuning expertise
- Proficiency with Power BI, Tableau, or other BI visualization tools
- Experience with big data frameworks (PySpark, Spark) for large‑scale data processing
- Advanced data visualization skills beyond standard BI dashboards