Advanced Data Scientist (Finance)
YOU MUST HAVE:
- Qualified applicants must have a Master's degree or foreign equivalent in Computer Science, Data Science, Engineering, or a related field and 2 years of experience with data science development, deployment in a commercial setup.
- Full term of experience must include: Programming Proficiency in Python/R and SQL; MachineLearning: Regression, random foreast, Xgboost, neural networks (RNN, GNN etc), MLOps, SVM, hyperparameter tuning, forecasting (arima, LSTM etc.), Optimization, Predictive Analytics, deep learning, Pytorch, tensorflow, keras; Stack proficiency with Azue and Databricks; experience in developing production level codebase leveraging standard practices including but not limited to the use of object oriented programming and version control; excellent understanding of Machine Learning techniques and proficiency in feature analysis, algorithm selection and model hyperparameter tuning.
- Telecommuting permitted two times per week.
Responsibilities:
- Responsible for leading technical machine learning projects and provide direction on exploring and implementing methodologies.
- Drive machine learning operation best practices across Forge Insight team and Honeywell.
- Lead critical machine learning initiatives and drive technical excellence within the organization.
- Function as an AI/ML expert and drive Forge Insight team to continue perform as AL Center of Excellence for Honeywell.
- Drive requirement for the Forge platform development to ensure it meets the AI/ML specifications and functional need.
- Work with Engagement managers to translate business requirements to technical tasks.
- Drive collaboration with colleagues across multiple function groups (Data Science, Data Engineering, domain experts) on unique challenges across different business units of Honeywell.
- Mentor entry level data scientists, machine learning engineers and data engineers to groom their technical skill for future development.
- Lead by example with upskilling as part of the AI/ML upskilling program of HCE.
- Develop complicated, scalable and robust analytics solutions to solve business problems.
- Leverage distributed training systems to build scalable machine learning pipelines for model training and deployments in IT/OT Products space.
- Design and implement solutions to optimize distribute training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks.
- Ensure ML Model performance, uptime, and scale, maintaining high standards of code quality and thoughtful design quality and monitoring.
- Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks.
- Lead technical discussion, evaluate, visualize and communicate the AI/ML model results and correlation with business KPI's.
- Work within project planning constraints, communicating any identified project risks and issues to the delivery/project manager and provide inputs to the change control process.
- Work closely with the operations team to change, optimize existing Automation and AI/ML processes and help build effective model performance monitoring, alert and notification framework to proactively identify problems/issues.
Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments - powered by our Honeywell Forge software - that help make the world smarter, safer and more sustainable.
