Delivery Intern & Product Intern - PFE 2027

About us:

At Cognira, we strongly believe that people are the biggest asset of our company. Our hand-picked team consists of passionate, collaborative, and forward-thinking individuals from all over the globe. We are passionate about making science easy and accessible to retailers, helping them get more value from people, data, and systems. We bring together expertise in retail, science, and scalable technologies to automate and enhance the quality of decision-making through software and consulting services.

We are proud to have a growing team of domain experts and data scientists, as well as a culture that fosters strong and long-lasting relationships with our clients.

Our Locations:

Atlanta, GA, US

Tunis, Tunisia

Istanbul, Turkey

Paris, France

Porto, Portugal

Our values:

Be nice and trustworthy

Put our customers first

Succeed together

Never stop improving

Are you ready to grow with us? To find out more about Cognira, please visit our website at www.cognira.com

Please submit your resume in English.

I- Delivery Team Internship Topic:

Data Science Consulting

Project Overview

Support senior Data Science Consultants in the implementation and delivery of forecasting solutions for new clients. This internship provides hands-on experience with data onboarding, forecasting, analytics, and client-facing deliverables in a retail and consumer goods environment.

Responsibilities

Validate client data feeds against the Cognira Data Framework (CDF), including completeness checks, data mappings, and promotion detection.

Develop and test Databricks notebooks and jobs for data ingestion and feature table generation.

Analyze forecast accuracy and assist with model tuning for seasonal trends and category-specific exceptions.

Build Merchant Insights dashboards and prepare weekly business reports.

Create release test cases and verify results following software releases.

Prepare data exploration analyses, insights, and client-facing presentations.

Expected Outcomes

Improved data quality and validation processes for new client implementations.

Reliable data ingestion and feature engineering pipelines in Databricks.

Enhanced forecast performance through model evaluation and tuning.

Actionable dashboards and reports that support business decision-making.

Comprehensive testing and validation of product releases.

Clear and effective communication of analytical findings to clients and stakeholders.

Required Skills

Experience with data analytics and reporting.

Proficiency in Python (Pandas) and SQL.

Understanding of statistics and forecasting concepts, including regression, time series analysis, MAPE, and bias.

Strong data validation and quality assurance skills.

Excellent written and verbal English communication skills.

Preferred Skills

Experience with Databricks, PySpark, and Delta Tables.

Knowledge of Retail or CPG (Consumer Packaged Goods) industries, including promotions, pricing, and merchandising.

Dashboard development and data visualization experience.

Familiarity with Git and GitHub.

Experience using AI coding assistants (e.g., Claude Code) for analysis and testing.

Understanding of causal inference methods such as Difference-in-Differences.

I- Product Team Internship Topic:

AI-Powered Release Documentation

Project Overview

Product teams often spend a significant amount of time turning information scattered across tickets, specifications, design files, and meeting decisions into release notes, feature guides, and knowledge base content. This process is typically manual, repetitive, and inconsistent, becoming increasingly difficult to manage as products evolve.

This project aims to design and implement AI-assisted workflows that automatically generate, validate, and maintain product documentation using existing tools and data sources. The goal is to enable Product Managers to review and approve documentation rather than create it from scratch, leaving behind a repeatable and scalable process for the team.

Deliverables

Map the current documentation workflow, including sources, ownership, cadence, and existing gaps, and develop a prioritized automation roadmap.

Build an internal release notes workflow that generates draft release notes from Jira release scopes for Product Manager review.

Design and maintain a structured knowledge base for Cora that is refreshed with each release, along with a validated question-and-answer benchmark to measure Cora's accuracy. (Content ingestion will be handled by the Cora Engineering team.)

Implement automated checks to identify inconsistencies between user stories, configuration specifications, and published documentation.

Develop AI-powered Product Management and UX assistants for recurring tasks, such as generating design specifications from user stories. As a stretch goal, build a backlog management assistant for duplicate detection and backlog grooming.

Create documentation, runbooks, and handover materials that enable the team to operate and maintain all workflows independently, and present a final demonstration with an assessment of time savings and quality improvements.

Required Skills

Strong structured thinking and the ability to break down processes into clear inputs, outputs, and workflows.

Excellent written communication and technical documentation skills in English.

Hands-on experience building AI solutions beyond basic chat interfaces (e.g., custom assistants, agents, prompts, workflows, or automation scripts).

Ability to critically evaluate and improve AI-generated content.

Strong sense of ownership, autonomy, and problem-solving.

Background in Product Management, Business Analysis, Business Informatics, or a related field.

Preferred Skills

Experience working with LLM APIs and agent frameworks (e.g., OpenAI, Anthropic, MCP).

Understanding of knowledge bases, Retrieval-Augmented Generation (RAG), and information retrieval systems.

Experience evaluating LLM outputs using benchmark question sets and accuracy metrics.

Basic Python skills for scripting and API integrations.

Familiarity with Jira, Confluence, or similar collaboration tools.

UX and user-centered design thinking.