Head of Data Science

As a Data Science Manager/Head, you will play a pivotal role in analyzing historical data to reveal trends and patterns through descriptive analytics
- Collaborating closely with business users and data analysts, you will translate business requirements into clear scopes, constructing prototypes and data science solutions
- Your responsibilities will include building end-to-end data science models, working on complex machine learning problems across various business functions, and collaborating with ML Engineers for the deployment and monitoring of models
- Conducting machine learning tests and experiments, extending existing ML libraries, and sharing insights through data visualization tools are integral aspects of the role
- Your proactive communication of complex ML solutions to stakeholders
and technology leaders will be crucial in maximizing the effectiveness of ML initiatives

Job Description:
- Analyze historical data to uncover trends and patterns as part of descriptive analytics
- Work closely with business users and data analysts to understand business requirements and project requirements
- Translate business requirements into clear scope and targets, construct a prototype for proof-of-concept (PoC) and data science solutions
- Build end-to-end data science models including Problem Definition, Data Collection, Data Cleaning, Exploratory data analysis, Features Engineering, Model Development, Model Evaluation, Model Tuning, Model Deployment, Performance Metrics, Documentation and Reporting, Maintenance and Monitoring
- Work with teams from different business functions and divisions to solve complex machine-learning problems
- Work closely with Machine Learning (ML) Engineers to deploy data science models into scalable production systems and monitor to track the performance of the models
- Run machine learning tests and experiments, and document findings and results
- Train, retrain, and monitor machine learning systems and models as needed
- Extend existing machine learning libraries and frameworks as needed
- Share insight with interested parties through data visualization tools like graphs, reports, charts, and dashboards
- Coordinate analytical activities, do research and propose solutions and strategies to address business challenges
- Communicate complex machine learning solutions, concepts, and the results of analyses in a clear and effective manner to business stakeholders and technology leaders to maximize the effectiveness of ML initiatives

Job Requirements:
- Experience with Data Science Project Life Cycle and various Machine Learning algorithms
- Proficiency in Python, SQL and Machine Learning libraries
- Excellent academic background with advanced degrees in Computer Science, Engineering, Mathematics, Statistics, or related Field
- Proven track record in designing, developing, and deploying Data Science/Machine learning models that created business impact
- At least 5 years of work experience in data science solutions implementation (full-stack development experience preferred though not mandatory)
- At least 3+ years of experience in developing Machine Learning infrastructure and MLOps in the cloud using AWS SageMaker or Databricks
- Create automation to automate the deployment of Data Science models on AWS SageMaker
- Experience working with ML frameworks like TensorFlow, Keras, SickitLearn, XGBoost, SparkML or similar
- Having an understanding of FinTech services products, and Fintech lending will be an advantage
- Ability to think critically and leverage domain expertise to build machine learning-powered applications that help to achieve high-level business objectives