Data Scientist - PM

We are invent.ai!
invent.ai is a fast-growing company providing AI-Decisioning for Inventory, Merchandising and Pricing Optimization Solutions to retailers around the world. This is an exciting time at invent.ai, as we are growing our client base, our product portfolio, and our teams to revolutionize the supply chain planning industry.
We are a group of bright, kind, and goal-oriented people working together to make retail better. We take pride in what we do. As a provider of award-winning enterprise-AI SaaS solutions that serve a growing international business with customers across the U.S., Europe, the Middle East, and East Asia, we make a measurable and meaningful difference for our customers every day.
Headquartered in the United States and with offices around the world, our team of 350+ people consists of young engineers and data scientists from top CS and engineering programs, collaborating across our Istanbul, Ankara, Philadelphia, and Amsterdam offices.
For more information, visit www.invent.ai
About Role
As a Data Scientist or Junior Data Scientist at invent.ai, you will play a key role in designing, developing, delivering, and maintaining data-driven solutions and products. You will work closely with product managers, fellow data scientists, and engineers to uncover insights, build models, and implement algorithmic solutions that directly impact our clients' operations.
The final role level will be determined during the interview process based on experience and demonstrated capability. Data Scientist profiles are typically expected to bring 2+ years of relevant experience and demonstrated AI proficiency, while Junior Data Scientist profiles may include strong fresh graduates who demonstrate solid technical fundamentals, problem-solving ability, and learning potential.
You will work in an AI-augmented environment where AI coding assistants and agents are part of the experimentation, modeling, and productionization loop.
What You Will Do
Design and implement machine learning and optimization models to solve real-world business problems
Leverage data and customer feedback to identify opportunities for enhancing features, improving performance, and supporting the development of new features to further optimize and evolve our existing product based on user needs.
Conduct proof of concept studies and develop prototypes to test new designs and features, making sure they are effective, reliable, and can scale to meet both current and future business needs.
Monitor key metrics to assess performance and product health.
Analyze complex datasets to uncover trends, patterns, and insights.
Create visualizations and dashboards to communicate findings to both technical and non-technical audiences
AI-Augmented Responsibilities
Use AI coding assistants (Claude, Cursor, GitHub Copilot) for the majority of day-to-day modeling, feature engineering, SQL, and pipeline code — with rigorous Human-in-the-Loop validation of outputs.
Design agentic ML workflows: multi-step pipelines where AI agents run experiments, compare configurations, and produce experiment summaries.
Use LLMs to accelerate EDA, feature ideation, and literature review on forecasting and optimization techniques.
Build LLM-assisted explainability: agents that translate model outputs and forecasts into client-ready narratives.
Automate at least two recurring data science chores per quarter (e.g., eval-report generation, dataset QA, model cards) with AI.
Contribute high-quality prompts, skills, and agent configs to the company company prompt library.
AI Expectations for This Role
Our AI expectations vary based on role level.
For Data Scientist profiles, we expect demonstrated experience using AI tools in day-to-day technical workflows and the ability to progress toward L2 (Agentic) maturity.
For Junior Data Scientist profiles, advanced AI proficiency is not required at entry. We look for curiosity, adaptability, and the ability to build these capabilities after joining.
AI Maturity Framework
We evaluate and develop everyone against a four-level AI maturity framework:
L0 — Transactional: Uses AI only as a search engine. Not the level we hire at.
L1 — Copilot / Augmented: Uses AI for autocomplete, partial generation, and day-to-day assistance. Minimum entry level.
L2 — Agentic: Delegates isolated tasks to AI agents and runs multi-step AI workflows. Our standard target.
L3 — Spec-Driven: AI-orchestrated execution from requirement to deployment. Expected of senior and lead roles.
Required AI Core Competencies
Agentic Problem Solving — you can break a multi-step task into pieces and orchestrate AI agents to execute them end-to-end.
Prompt Engineering — you manage context effectively and design prompts that produce reliable, high-quality outputs.
Automation Maturity — you actively hunt for repetitive manual work in your own workflow and eliminate it with AI.
Human-in-the-Loop Management — you validate AI outputs, recognize when the model is wrong, and document corrections.
AI Tool Ecosystem Fluency — you are comfortable across the current tool stack: Claude, Cursor, ChatGPT/GPT-4o, Gemini, GitHub Copilot, and MCP (Model Context Protocol) for connecting AI to internal systems.
On-the-Job AI Expectations
Before starting any task, ask: "Can I approach this differently using AI?"
Identify the manual and repetitive parts of your workflow and automate them.
Use AI tools (Claude, Cursor, ChatGPT, Gemini, and any team-approved tools) as part of your daily craft.
Validate AI outputs and keep a Human-in-the-Loop habit: when AI gets it wrong, notice it, fix it, and learn from it.
Share your best prompts and AI workflows with the team.
Participate in our monthly AI workshops and quarterly AI assessment sessions.
Reach your target AI Maturity Level within 6 months of starting.
Qualifications
What We Are Looking For
BSc or MSc in a quantitative field (e.g., Industrial Engineering, Computer Science, Electrical Engineering, Mathematics, etc.)
Solid understanding of data structures, mathematical modeling, and statistical methods
Excellent analytical thinking, problem-solving skills, and attention to detail
Proficiency in programming, scripting, and query languages such as Python, R, and SQL
Familiarity with software development, deployment, and version control tools
Strong communication skills and fluency in English
Eagerness to learn, take initiative, and be part of a collaborative team
For Data Scientist Profiles
Minimum 2 years of relevant professional experience
Demonstrated practical AI proficiency
For Junior Data Scientist Profiles
Open to strong fresh graduates and early-career candidates
No minimum professional experience requirement
Strong technical fundamentals and demonstrated potential through interviews, academic work, internships, or projects
Preferred Qualifications
Academic background from a reputable or top-tier university is a plus
Experience with PySpark, Tableau, or Airflow
Experience with optimization models and modeling languages (PuLP, Pyomo, Gurobipy, GAMS, Gurobi, CPLEX) is a big plus.
Experience with cloud platforms such as Databricks, AWS, GCP or Azure.
Exposure to supply chain, retail analytics, or large-scale optimization problems, forecasting.
Exposure to object-oriented programming (OOP) fundamentals.
Military service completion or deferral (for male candidates in Türkiye)
Required AI-First Qualifications
For Data Scientist candidates, we look for practical experience with AI coding assistants, prompt engineering, Human-in-the-Loop validation, and AI-assisted workflows.
For Junior Data Scientist candidates, these are considered development areas rather than mandatory entry requirements. Strong fundamentals, learning agility, and willingness to adopt AI-first ways of working are key.
Current AI Maturity at L1 (Augmented) minimum, with a clear 6-month path to L2 (Agentic).
Daily user of AI coding assistants (Claude, Cursor, GitHub Copilot); able to show concrete prompts and outputs from real projects.
Prompt engineering skill, including context management for long codebases and long data schemas.
Human-in-the-Loop discipline: you can describe a real example where AI gave you the wrong answer and how you caught it.
Exposure to agentic workflows or MCP-based integrations is a plus.
Willingness to teach and share AI practices with teammates.
Life at invent.ai
Join great teammates across the globe: We consider ourselves anything but average!
We love what we do: Our team has an exciting tech stack; Pyspark, Databricks, Python, and so on
Flat organization with a ve

Work arrangement
Yes