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Senior Data Scientist

3 months ago

Whitehouse Station, New Jersey, United States Chubb Full time

NA Field Analytics - Sr. Data Scientist

Job Summary

Chubb is the world's largest publicly traded property and casualty insurer. With operations in 54 countries, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.

Chubb's North America Field Analytics team is seeking a highly skilled Senior Data Scientist to join our fast-paced and dynamic environment. As a key member of our team, your primary responsibility will be delivering exceptional analytics solutions to our field organization across North America.

To excel in this role, you must possess the ability to translate business needs into cutting-edge analytic products, leveraging the latest technologies. Managing a demanding workload and ensuring timely delivery on both ad hoc and long-term projects is a vital requirement for success in this position. Collaboration with key business partners and peers is crucial given the complexity of tasks within the Field.

You will have the opportunity to work with a variety of internal and external data sources, developing, deploying, and monitoring high-quality model solutions. This role calls for a proactive and self-driven individual who thrives on managing and delivering projects independently. Balancing multiple priorities, achieving high-quality outcomes, and maintaining intellectual curiosity are key traits for success in this role. By taking ownership of project portfolios and collaborating with stakeholders, you will play a critical role in promoting data-driven decision-making and achieving our business objectives.

As a Senior Data Scientist in this role, you will have the following responsibilities:

  • Undertake end-to-end analytics projects, either independently or in collaboration with other data scientists and cross-functional teams.
  • Take the lead in understanding business requirements and translating them into effective analytics solutions.
  • Execute exploratory data analysis, including data cleanup and preprocessing, to extract meaningful insights.
  • Propose and implement appropriate approaches for model development, evaluating various algorithms and techniques.
  • Support model deployment and monitoring to ensure smooth integration into existing systems.
  • Embrace a nimble approach to problem-solving, effectively managing resources, technology, and delivery timelines.
  • Identify the most suitable approaches to address specific business challenges, creating and testing quick proof-of-concepts (POCs) before proceeding with full-scale solutions.
  • Conduct research to identify and recommend statistical and mathematical methodologies that are best suited for tackling each business problem.
  • Demonstrate intellectual curiosity and drive to continually learn and expand expertise in the field of data science.
  • Take ownership of portfolios of projects, effectively juggling multiple priorities while delivering high-quality results in a timely fashion.
  • Collaborate and communicate effectively with key stakeholders, both technical and non-technical, through written reports and presentations.
Qualifications Required
  • Problem-Solving: Excellent problem-solving skills with a demonstrated ability to approach complex challenges with creativity, critical thinking, and attention to detail.
  • Proficiency in Python: Demonstrated expertise in Python programming, including strong knowledge of core Python libraries such as NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch
  • Exploratory Data Analysis (EDA) and Complex/Incomplete Data Manipulation: Proficiency in conducting Exploratory Data Analysis (EDA) to understand data patterns, identify outliers, and derive meaningful insights. Experience in handling complex and incomplete datasets, including data cleaning, imputation, and dealing with missing values. Knowledge of advanced techniques such as feature engineering, dimensionality reduction, and handling imbalanced data. Ability to work with unstructured and semi-structured data formats.
  • Machine Learning: Proven experience in developing and implementing machine learning algorithms and models, including both supervised and unsupervised learning techniques. Solid understanding of regression, classification, clustering, and anomaly detection algorithms.
  • Large Language Model Fine-Tuning: Experience in fine-tuning large language models such as GPT and Llama. Ability to preprocess and prepare data for language model training and fine-tuning. Understanding of transfer learning techniques and model adaptation.
  • Statistical Analysis: Strong statistical analysis skills, including hypothesis testing, experiments, and A/B testing. Familiarity with statistical models and methodologies for data analysis and prediction.
  • Deep Learning: Familiarity with deep learning architectures and frameworks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Generative Adversarial Networks (GANs). Experience in implementing deep learning models using TensorFlow, Keras, or PyTorch.
  • SQL Proficiency: Strong proficiency in SQL for data querying, manipulation, and analysis. Ability to write complex SQL queries involving multiple tables, joins, subqueries, and aggregations. Familiarity with optimizing SQL queries for performance
  • Data Engineering: Understanding of data engineering principles and ability to work with large-scale datasets. Knowledge of distributed computing platforms like Spark. Familiarity with data pipelines and ETL processes is advantageous.
  • Communication and Collaboration: Strong communication skills, both written and verbal, with the ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders. Proven experience collaborating within cross-functional teams.
  • Education:
  • Degree in data science or related field
Location We are open to the following work locations for this position

(Philadelphia, PA - Jersey City, NJ - Whitehouse Station, NJ).

Please note there will be limited expected travel to various work locations for in person collaboration and meetings.

In Jersey City, NJ the pay range for the role is $87,500 to $149,500. The specific offer will depend on an applicant's skills and other factors. This role may also be eligible to participate in a discretionary annual incentive program. Chubb offers a comprehensive benefits package, more details on which can be found at This range is specific to Jersey City, NJ and may not be applicable to other locations.