Position:
Junior Data Analyst
Company:
Daice Labs
Location:
India (Remote)
Job type:
Full-time
Job mode:
Remote
Job requisition id:
Not provided
Years of experience:
0–2 years
Company description
Daice Labs represents a pioneering force in the global technology landscape, having been established by visionary scientists hailing from the prestigious MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
The organization is dedicated to the development of sophisticated hybrid Artificial Intelligence infrastructure that is specifically engineered for long-term, adaptable, and fully verifiable work processes.
By masterfully integrating state-of-the-art frontier models with neurosymbolic methodologies and bio-inspired system designs, Daice Labs empowers human teams to effectively collaborate, govern, and maintain co-ownership of innovative outcomes.
The company operates as a specialized operating system for continuous collaboration, offering persistent context and domain-specific environments that are essential for modern tech development.
The core philosophy of the company is built upon an adaptive learning framework that encourages the creation of reusable and transferable solutions across a multitude of technical domains.
Daice Labs addresses the fundamental "brittleness" of current AI systems, focusing on solving issues related to context retention, retraining costs, auditability, and alignment with human value creators.
The infrastructure provided by the firm supports governed work where human teams supervise and co-build products, discoveries, and systemic innovations.
A unique aspect of their business model involves providing compute and support for selected projects while allowing builders to retain the majority of commercial upside, fostering a true spirit of cooperative innovation.
The name "Daice" (pronounced like "dice") symbolizes the dual nature of exploration and programmable adaptation that defines their approach to technology.
As a research-driven services organization with a specialized team of 11-50 employees, they maintain a high-impact environment focused on the future of AI.
Profile overview
The Junior Data Analyst role at Daice Labs is an exceptional entry-level opportunity designed for individuals who are passionate about the intersection of data science and artificial intelligence.
This position is specifically structured to support data collection, comprehensive analysis, and detailed reporting across the company's research and product operations divisions.
The ideal candidate will be an early-career professional looking to apply their quantitative analytical skills within a high-growth, AI-focused environment.
As a Junior Analyst, you will play a pivotal role in helping the team move from raw data to actionable insights that drive product and research strategies.
The role offers a remote-first culture based in India, providing the flexibility to work from anywhere while contributing to a global team of MIT scientists.
Successful candidates will be responsible for managing both structured and unstructured data, ensuring that information flows smoothly through the company's internal systems.
You will be tasked with building and maintaining critical dashboards that stakeholders rely on to track AI model performance and operational health.
The profile demands a high level of curiosity, as you will be expected to conduct market research and competitive analyses to keep the company ahead of industry trends.
Effective communication is a cornerstone of this profile, as the analyst must document complex methods and findings in a way that is accessible to both technical and non-technical peers.
This is a career-defining role that provides direct exposure to neurosymbolic AI, bio-inspired design, and the next generation of collaborative infrastructure.
Qualifications
Applicants must hold a Bachelor’s degree in a highly quantitative or technical field, including but not limited to Statistics, Mathematics, Economics, or Computer Science.
Degrees in Engineering or other disciplines with a heavy emphasis on data-driven decision-making and mathematical modeling are also highly valued.
The position is open to candidates with 0 to 2 years of professional experience, making it a perfect fit for recent graduates or those with early career internships.
A strong academic background with projects focused on data manipulation, statistical analysis, or predictive modeling is considered a significant asset.
Technical proficiency in SQL is required for querying databases and managing internal data structures effectively.
Hands-on experience with Python or R is essential for performing advanced quantitative and qualitative analyses.
Candidates must demonstrate the ability to work comfortably with both structured datasets and more complex, unstructured data formats.
Attention to detail is a non-negotiable requirement, as the role involves ensuring the accuracy of AI model performance benchmarks and data summaries.
Strong organizational skills are necessary to manage multiple tasks and deadlines within a fast-paced, remote work environment.
Fluency in written and verbal English is required to maintain clear documentation and effective collaboration with international team members.
Additional info
Daice Labs is committed to providing a workspace where data-driven insights are at the heart of every decision, fostering a culture of continuous learning and professional growth.
The remote nature of this role requires a candidate who is self-motivated and capable of maintaining high productivity without direct physical supervision.
Familiarity with data visualization platforms such as Tableau, Power BI, or Looker is preferred and will help the analyst present findings more effectively.
Experience using Python-specific data libraries like Pandas and NumPy is a major plus for anyone looking to excel in this technical environment.
Exposure to machine learning concepts or working within an AI product development lifecycle will give candidates a competitive edge during the selection process.
The company utilizes modern project management and documentation tools like Notion, Linear, and Confluence, so prior experience with these is beneficial.
As an equal opportunity employer, Daice Labs values diversity and encourages applications from all backgrounds regardless of race, religion, sex, or disability.
The compensation package for this full-time role is competitive within the Indian market and is tailored to the candidate's skills and previous experience.
Joining Daice Labs means becoming part of a community that explores "what to build and why" rather than just focusing on the mechanics of construction.
The collaborative environment ensures that every analyst's work contributes directly to shared primitives—reusable building blocks that advance the state of AI.
Technical Analysis of Data Flows
The data lifecycle at Daice Labs begins with the ingestion of diverse datasets from both internal repositories and external research sources.
The Junior Data Analyst will oversee the initial cleaning phase, where raw data is transformed into a usable format through rigorous validation and normalization processes.
Analytical tasks will include performing regression analyses, time-series forecasting, and qualitative assessments of AI model outputs to ensure reliability.
The reporting infrastructure is built on a foundation of transparency, requiring the analyst to create visual summaries that highlight key performance indicators (KPIs).
Stakeholder engagement involves regular meetings with research scientists to define the scope of data requirements for upcoming AI model training sessions.
Performance benchmarking is a critical component, where model accuracy is measured against industry standards and neurosymbolic benchmarks.
The analyst will assist in the development of "persistent context" mechanisms, ensuring that data used in collaboration remains accessible and relevant over long horizons.
Market research efforts will focus on tracking the evolution of hybrid AI infrastructure and identifying gaps in the current competitive landscape.
Literature reviews will involve synthesizing academic papers from sources like MIT CSAIL to inform the company’s ongoing research and development.
Documentation standards at Daice Labs require meticulous records of all analytical assumptions, scripts, and findings to support the co-ownership and attribution layer.
Operational Excellence and Strategy
Operating within a neurosymbolic framework requires a unique blend of symbolic reasoning and deep learning expertise, which the analyst will support through data preparation.
The bio-inspired system design at Daice Labs mimics natural adaptability, requiring the data analyst to monitor how systems improve and generalize over time.
Collaboration with the engineering team ensures that the data pipeline is scalable and integrates seamlessly with the sandboxed execution environment.
Risk management is addressed through the verification layer, where the data analyst helps validate that AI outputs align with human intent and governance protocols.
Product operations rely on the analyst’s ability to interpret user feedback and system logs to identify areas for feature enhancement or UI/UX improvements.
The "continuous learning" aspect of the infrastructure means the analyst will be involved in feedback loops that retrain and refine models based on real-world performance.
Resource allocation, particularly compute power, is often guided by the data insights provided by the analytics team to maximize ROI for specific projects.
The co-ownership layer involves complex data tracking for attribution, ensuring that all contributors to an AI outcome are properly recognized and rewarded.
Remote operations are supported by a suite of digital communication tools that ensure the India-based team remains synchronized with global goals.
Strategic initiatives often stem from the analyst's discovery of patterns in one domain that can be successfully transferred to another through adaptive discovery.
Skills and Competencies for Success
Beyond technical skills, the analyst must possess a high level of critical thinking to question data sources and identify potential biases in AI outputs.
Problem-solving in a "brittle" AI environment requires an innovative mindset to build more resilient and flexible data processing workflows.
Time management is essential for balancing day-to-day reporting tasks with long-term research projects and market analysis.
Adaptability is key, as the underlying models and technologies at Daice Labs evolve rapidly in line with the latest breakthroughs in AI research.
The ability to summarize complex statistical findings into executive-level briefings is a skill that will be honed through regular interaction with the leadership team.
Teamwork is facilitated through shared primitives, requiring the analyst to write modular code that can be easily understood and reused by others.
A focus on "long-horizon work" means the analyst must be comfortable with projects that develop over months rather than days, maintaining consistent data integrity throughout.
Understanding the economic implications of AI, such as commercial upside and market positioning, helps the analyst provide more relevant business insights.
Attention to governance and ethics ensures that all data collection and analysis comply with international standards and the company's internal co-ownership policies.
A passion for the MIT CSAIL philosophy of pushing the boundaries of what is possible in computer science will drive the analyst to excel in this rigorous environment.
The Future of AI Infrastructure at Daice Labs
Daice Labs is building the substrate for the next generation of AI, moving away from standalone models toward integrated, governed ecosystems.
The Junior Data Analyst will witness firsthand how composite systems pair frontier models with symbolic logic to solve complex reasoning problems.
Adaptive learning frameworks allow the company's OS to learn from every interaction, a process that is fueled by high-quality, curated data.
The focus on verification ensures that AI remains a tool for human empowerment rather than an uncontrollable black box.
By fostering cooperative innovation, Daice Labs is creating a model where individual builders can leverage enterprise-grade infrastructure to create massive value.
Domain-specific environments allow for the customization of AI tools for specialized fields like healthcare, finance, or legal services, all supported by data analysis.
The sandboxed execution layer provides a safe space for testing new AI primitives before they are integrated into the broader compositional substrate.
Compound capabilities mean that every project the analyst works on makes the overall system smarter and more capable for the next challenge.
The "operating system for continuous collaboration" is designed to be the backbone of future remote-first, high-tech enterprises.
As the company grows, the data analyst will have the opportunity to move into specialized roles in machine learning engineering, product management, or research science.
Impact and Value Creation
The analyst’s work in evaluating AI outputs directly influences the safety and reliability of the products released by Daice Labs.
By documenting patterns and discovered primitives, the analyst helps build a library of shared knowledge that accelerates all future development.
Competitive analysis provided by the analyst informs the leadership's decisions on which market segments to target for the next phase of growth.
Data-driven summaries help identify bottlenecks in the product development lifecycle, allowing for more efficient resource management.
Research into bio-inspired design leads to more efficient AI systems that require less compute power for the same level of performance.
The analyst's role in attribution ensures that the co-ownership model remains fair and transparent for all stakeholders.
Supporting product operations means the analyst is directly involved in the success of the builders who use Daice Labs infrastructure.
Quantitative reports on model performance benchmarks provide the evidence needed to attract new partners and investors to the platform.
The collaborative nature of the role ensures that insights from the India team are integrated into the core product strategy at the global level.
Ultimately, the Junior Data Analyst helps bridge the gap between complex MIT-level research and practical, governed AI applications that solve real-world problems.
Career Path and Mentorship
Junior Analysts at Daice Labs are mentored by world-class scientists, providing an unparalleled learning environment in the field of AI and Data Science.
The company encourages active participation in research discussions, allowing even entry-level employees to contribute to high-level strategic planning.
Training programs focus on advanced SQL techniques, Python optimization, and the latest trends in neurosymbolic AI.
The analyst will gain hands-on experience with the entire data stack, from ingestion and ETL to visualization and predictive modeling.
Promotion pathways are clear, with opportunities to advance into Senior Analyst, Data Scientist, or Lead Research roles based on performance and interest.
The remote-first culture is supported by regular virtual team-building events and knowledge-sharing sessions to ensure a strong sense of community.
Employees are encouraged to pursue professional certifications and attend relevant tech conferences to stay at the cutting edge of the industry.
The diverse team structure provides exposure to various disciplines, including software engineering, system design, and commercial strategy.
Feedback cycles are frequent and constructive, focusing on both technical skill development and soft skills like communication and leadership.
Joining Daice Labs at the junior level offers the chance to be an early employee at a company that is fundamentally changing how AI is built and governed.
Please click here to apply.
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