The position will be located in Norwalk, CT.

Position Overview:

As a Data Scientist at Mitsubishi HC Capital, INC. (MHCCNA), you will play a pivotal role in extracting meaningful insights from complex datasets to drive strategic decision-making and improve business outcomes. You will collaborate closely with cross-functional teams to identify opportunities, develop analytical solutions, and deploy predictive models. This role assumes accountability for ensuring dependable, efficient, and secure operation and advancement of MHCCNA’s advanced analytics platform to drive crucial business needs.

Commitment to Internal Control:

The Data Scientist is required to possess a comprehensive understanding of and adhere to the system of internal controls associated with the fundamental duties and responsibilities of the role. This includes compliance with SOX and all other pertinent regulatory and compliance policies and requirements.

Essential Duties and Responsibilities:

The responsibility of Data Scientist encompasses the entire lifecycle of the Advanced Analytics environment that underpins the vital business requirements of MHCCNA. This includes the design, development, implementation, operation, and ongoing support of these critical systems.

The role necessitates the capacity to explore and grasp emerging technologies while collaborating closely with peer teams to establish strategic roadmaps and priorities. The ability to swiftly acquire and proficiently apply hands-on administration skills is essential. As a Subject Matter Expert in technical requirements, this position will play a crucial role in supporting and implementing data projects, as well as engaging effectively with users and other IT staff.

The Data Scientist responsibilities include:

• Lead the design and development of enterprise-grade deep analytics.
• Explore and analyze large, complex datasets to identify patterns, trends, and correlations.
• Develop hypothesis-driven analyses to uncover actionable insights.
• Design, develop, and deploy predictive models and machine learning algorithms to address business challenges.
• Optimize model performance through feature engineering, parameter tuning, and validation techniques.
• Communicate findings and recommendations to stakeholders through clear and compelling data visualizations, reports, and presentations.
• Collaborate with teams across the organization to translate data-driven insights into actionable strategies.
• Stay abreast of the latest advancements in data science, machine learning, and analytics technologies.
• Implement automation and streamline processes to optimize the entire data and analytics platform, ensuring efficient throughput and high-performance outcomes.
• Recognize, devise, and execute internal process enhancements, including automation of manual tasks, optimizing data delivery, and redesigning architecture or infrastructure to enhance scalability.
• Collate large, intricate datasets that align with functional and non-functional business demands.
• Develop processes that facilitate data transformation, manage data structures, metadata, dependencies, and workload management.
• Collaborate with business users to understand functional and data requirements, contributing to the enhancement of data models and pipelines.
• Apply analytical and problem-solving skills to diagnose and resolve intricate technical issues.
• Create, maintain, and continuously enhance scalable data pipelines, while also developing new data source integrations to accommodate the growing volume and complexity of data.
• Designing, implementing, and managing data extraction, transformation, and loading (ETL) processes.
• Creating comprehensive technical specification documents and application interface designs.
• Creating data processing and integration solutions for both batch and real-time scenarios, proficiently handling structured and unstructured data.
• Participate in design discussions, code reviews, and project-related team meetings.
• Ensuring data security and compliance with relevant regulations and best practices in all data operations.
• Troubleshooting and resolving data and system issues, stepping in when necessary to address outages and challenges.
• Other duties and responsibilities as assigned or needed.

KPI’s (Key Performance Indicators ):

• Deliver Data Science solutions that are 99% defect-free providing that adequate written business requirements, development time, and business test review were afforded during the project. This standard does not apply to legacy remediation efforts or ready to serve emergency production response activities.
• Effectively utilize consulting resources on all significant projects (over 40 hours) to allow for development power of scale. Consultants should do lower value work that is considered heavy lift, freeing up programmer analysts to spend more time in analysis and design while maintaining tight control over quality, code, and company intellectual property.
• These are overarching KPI metrics that are applicable to all goals that are defined over the course of the business year.

Responsibility and Decision-Making Authority:

• Exercise independent judgment and decision-making while adhering to Company Policy.

Management/Supervisory Responsibilities:

Qualifications/Competencies:

• Must have experience developing and deploying Data Science solutions leveraging components like Azure OpenAI and Azure Notebooks.
• Proven work experience, 12 years in Data Science or deep analytics in the financial sector.
• Proficiency in programming languages such as Python, R, or SQL with experience in data manipulation, statistical analysis, and machine learning.
• Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
• Strong expertise in data visualization tools such as PowerBI or Tableau.
• Excellent communication and collaboration skills, with the ability to distill complex technical concepts into understandable insights for non-technical stakeholders.
• Strong problem-solving skills and a passion for driving business impact through data-driven decision-making.
• Experience working with data integration techniques & self-service data preparation.
• Experience in requirements analysis, design, and prototyping.
• Experience with big data technologies such as Hadoop and Spark.
• Strong knowledge or advanced analytics techniques such as deep learning, natural language processing, or reinforcement learning.
• Experience deploying machine learning models into production.
• Thinking Skills – Cognitive Ability, Analytical Ability, Problem-Solving
• Technical / Professional – Detail Oriented, Thoroughness

Additional Benefits:

• Medical, Dental and Vision Plans
• 401(k) and matching
• Generous Paid Time Off
• Company paid Life Insurance
• Employee assistance program
• Training and Development Opportunities
• Employee discounts

The job description does not constitute an employment contract, implied or otherwise, other than an “at will” relationship and is subject to change by the employer as the needs of the employer and requirements of the job change.