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, U.S.

Paris, France

Istanbul, Turkey

Tunis, Tunisia 

Porto, Portugal

Our values:

Be nice and trustworthy

Put our customers first

Succeed together

Never stop improving

About this internship 
We're looking for highly talented & motivated interns to join our Engineering team and nail one of the following projects: 

1️⃣ Tiny Time Mixers for Demand Forecasting (TSMixer / Time-Series LLMs)
Build a robust data pipeline to convert structured sales + promotion data into multivariate inputs for modern time-series backbones. Implement a forecasting model using TSMixer-style architectures (IBM/Google variants), and evaluate zero-shot, few-shot and full-shot performance versus traditional regression baselines.

 2️⃣Advanced Modeling of Intermittent and Sparse Demand
Develop and benchmark models tailored for 'lumpy' products with long zero-demand intervals. Compare Croston variants and zero-inflated statistical approaches, then prototype sequential approaches (HMMs, Switching State-Space, and Deep State-Space Models using LSTMs/GRUs). Implement a two-part forecasting approach (event timing + non-zero magnitude) and propose metrics that reflect inventory and event-level performance.

3️⃣ LLM-Driven Feature Engineering for Knowledge-Augmented Retail Data
Design a Knowledge-Augmented Feature Pipeline (KAFP) to extract time-stamped, quantifiable exogenous features from unstructured sources (news, social media, competitor announcements, weather). Implement a Retrieval-Augmented Generation (RAG) pipeline and LLM prompt chains to generate signals like sentiment scores, trend indices, and competitive activity indices that can be joined into forecasting datasets.

You will be part of a high-growth software company. Our program is designed so interns can grow their skill sets, do meaningful work, and have a lot of fun along the way!

• Over the course of the internship, you will be exposed to a wide range of Cognira’s tools, techniques, and technologies and have the opportunity to gain credible experience and learning


• This internship will entirely be in-person for you to get an in-depth experience of the company's culture and be more involved throughout your tenure.


• Duration: 4-6 months.

This is what we're looking for:

• Excellent academics in Computer Science, Engineering, or related field


• Problem-solving is your jam, and you're all about critical thinking.


• You're not afraid to roll up your sleeves and get stuff done, even if you're independently on your own with minimal supervision.


• You can juggle multiple projects like a pro.


• Challenges don't scare you; in fact, you love diving into them.


• You can communicate like a champ, whether it's writing reports or presenting in a room full of people.


• You're curious, and you love picking up new skills & technologies.


• You're a team player, always up for sharing your ideas and best practices.
  

It's not just an internship; we've got some great added value for you too. Here's what you'll enjoy:

• Great company culture.


• "Learn and Share" sessions.


• You'll get support from your mentors.


• Social events and after-work.


• A flexible and fun work environment.


• Casual dress code.


• You'll work with a cool team! We respect your ideas, and we're all about trying new things.


• Work/life balance
  

[ Important: Please send us your resume in English only ]