Science Visualization Jobs in Boston, MA (NOW HIRING)

Company Description   
 The Ikigai platform unlocks the power of generative AI for tabular data. We enable business users to connect disparate data, leverage no-code AI/ML, and build enterprise-wide AI apps in just a few clicks. Ikigai is built on top of its three proprietary foundation blocks developed from years of MIT research - aiMatch, for data reconciliation, aiCast, for prediction, and aiPlan, for scenario planning and optimization. Our platform enables eXpert-in-The-Loop (XiTL) for model reinforcement learning and refinement, at scale.  

Rooted in academia, Ikigai also offers unmatched educational support that upskills workforces and transforms organizations into AI-driven ones. Ikigai Academy has already trained over 6000 students from 90+ countries on topics like “No Code AI” and “Time Series Forecasting”.  

Role Description: 

We are seeking a Data Scientist who thrives at the intersection of data transformation, analytics, and implementation. In this role, the Data Scientist will partner directly with clients to prepare, clean, and transform their data so it can seamlessly integrate with our aiCast platform. This Data Scientist will help customers unlock the value of their data—building pipelines, dashboards, and forecasts—and in many cases, own the end-to-end implementation from ingestion through delivery.  

This Data Scientist position is centered on data engineering, applied analytics, and client enablement. You’ll play a key role in helping customers get from raw data to actionable insights. 

Responsibilities: 

• Collaborate with client teams to understand their data sources, structures, and business requirements.  

• Design and implement data transformation pipelines to prepare data for aiCast ingestion.  

• Build reporting and dashboarding solutions to help clients interpret and act on their data.  

• Develop forecasting and analytics workflows to extend client use cases beyond data prep.  

• Support end-to-end solution delivery—covering extraction, transformation, validation, and deployment—when applicable.  

• Troubleshoot data quality issues and provide recommendations for improved structures or processes.  

• Partner with internal engineering and success teams to ensure smooth implementations and knowledge transfer.  

Qualifications: 

• At least 1-3 years of experience in a data role, preferably with enterprise clients   

• Associate’s or Bachelor’s degree in a relevant field (e.g., Computer Science, Statistics, or Applied Mathematics) preferred   

• Proficiency in Python, including libraries like NumPy and Pandas for data transformation.  

• Working knowledge of SQL for querying and joining datasets (intermediate level).  

• Experience building ETL/ELT pipelines or similar data processing workflows.  

• Familiarity with data visualization, reporting, or dashboarding tools.  

• Strong problem-solving skills with a focus on client outcomes and practical implementation.  

• Ability to work with incomplete or messy data and structure it for usability in production systems.  

• Excellent communication skills for translating technical work into business impact.  

Preferred Skills: 

• Knowledge of data transformation (e.g. using python Pandas library) database management, data integration, or data visualization is a plus   

• Experience with forecasting techniques or applied time-series analytics.  

• Background in client-facing or consulting roles where you owned implementation delivery.  

• Understanding of software engineering best practices (version control, testing, deployment).  
   

Ikigai Labs is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry or citizenship, ethnicity, disability, military or protected veteran status, genetic information, sexual orientation, marital or familial status, or any other personal characteristic protected under applicable law.  

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