Senior ML Engineer - Mumbai, India - Sri Sai Overseas Recruitment
Description
JOB SUMMARY
This role of ML Engineer is one of the four pillars within Enterprise Risk management and Specialty Risk team. The pillar aims to add value to underwriting, product, actuarial, portfolio management and claim teams by providing solutions and services across the value chain. It is within the mandate of the team to be at the forefront of analytics understanding by driving the development of novel solutions through the combination of risk knowledge and data science capabilities.
QUALIFICATIONS AND EDUCATION REQUIREMENTS
Experienced AI/ML/Data Science hands-on productive individual contributor and technical leader accustomed to collaborating with expert teams to solve complex analytical problems within complex environments.
Create evidence-based systems for support of human decision-making, automation, deep insight, and innovation.
Create new products, grow new markets, improve processes, enable automation, and reduce risk.
Knowledge and expertise in solving complex analytical, statistical, and engineering problems through modest size teams.
Knowledge of Information Systems Architecture, Software Engineering Algorithms in C#, Python or R.
Monte Carlo Simulation, Agent Simulation, Signal Processing, Graph analysis, and Distributed Computation.
Prediction/forecasting, classification, recommendation engines, driver/attribution analysis, optimization/planning, anomaly detection, detection/perception, behaviour modelling, decision support, information search, risk management, system modelling.
Deep personal expertise with a broad range of supervised/unsupervised/reinforcement learning algorithms, gradient boosted methods (e.g., XGboost, lightGBM), kNN/k-means/hierarchical clustering, regression, decision/isolation trees/forests, network/graph analysis, associate rule learning (apriori), deep learning, PCA, Natural Language Process
Advanced degree and education with quantitative background (computer science, applied mathematics, probability/statistics, actuarial science, engineering, econometrics, or related degrees).
Knowledge and expertise in machine learning, analytics tools, and data platforms (relational databases, SQL, and Power BI).
Strong experience in application of computer vision and aerial imagery to property insurance and advanced analytics a plus
Predictive modelling experience across the insurance value chain (marketing, pricing, underwriting, claims, capital modelling a plus).
Encourages a culture of Efficiency, Effectiveness, Innovation, Integrity, Humor, Kindness, and Teamwork.
5+ years of hands-on experience in executing projects with technical background & predictive analytics components.JOB SUMMARY
This role of ML Engineer is one of the four pillars within Enterprise Risk management and Specialty Risk team. The pillar aims to add value to underwriting, product, actuarial, portfolio management and claim teams by providing solutions and services across the value chain. It is within the mandate of the team to be at the forefront of analytics understanding by driving the development of novel solutions through the combination of risk knowledge and data science capabilities.
QUALIFICATIONS AND EDUCATION REQUIREMENTS
Experienced AI/ML/Data Science hands-on productive individual contributor and technical leader accustomed to collaborating with expert teams to solve complex analytical problems within complex environments.
Create evidence-based systems for support of human decision-making, automation, deep insight, and innovation.
Create new products, grow new markets, improve processes, enable automation, and reduce risk.
Knowledge and expertise in solving complex analytical, statistical, and engineering problems through modest size teams.
Knowledge of Information Systems Architecture, Software Engineering Algorithms in C#, Python or R.
Monte Carlo Simulation, Agent Simulation, Signal Processing, Graph analysis, and Distributed Computation.
Prediction/forecasting, classification, recommendation engines, driver/attribution analysis, optimization/planning, anomaly detection, detection/perception, behaviour modelling, decision support, information search, risk management, system modelling.
Deep personal expertise with a broad range of supervised/unsupervised/reinforcement learning algorithms, gradient boosted methods (e.g., XGboost, lightGBM), kNN/k-means/hierarchical clustering, regression, decision/isolation trees/forests, network/graph analysis, associate rule learning (apriori), deep learning, PCA, Natural Language Process
Advanced degree and education with quantitative background (computer science, applied mathematics, probability/statistics, actuarial science, engineering, econometrics, or related degrees).
Knowledge and expertise in machine learning, analytics tools, and data platforms (relational databases, SQL, and Power BI).
Strong experience in application of computer vision and aerial imagery to property insurance and advanced analytics a plus
Predictive modelling experience across the insurance value chain (marketing, pricing, underwriting, claims, capital modelling a plus).
Encourages a culture of Efficiency, Effectiveness, Innovation, Integrity, Humor, Kindness, and Teamwork.
5+ years of hands-on experience in executing projects with technical background & predictive analytics components.