Data Scientist

At Aftershoot, we’re building a global community of photographers who spend less time behind their screens and more time behind the lens. Every feature we create, every update we ship, is designed to make post-processing easier, faster, and smarter for photographers around the world.

From culling to editing to retouching. Our AI tools do all the heavy lifting, so photographers can focus on what they do best: capturing moments, telling stories, and creating magic.

We’re a fast-growing, passionate team backed over 100K+ active users globally. Double-digit ARR in less than 4 years, amazing investors and team who truly believe in our mission.

Our mission is clear: Become the default companion for every photographer in the world.

If you care deeply about building meaningful products, working with curious and driven teammates, and being part of a team that genuinely loves what they do, we’d love to meet you.

Let’s create the future of AI in photography, together.

As a Data Scientist, you'll be at the intersection of product, growth, and AI - turning ambiguous business challenges into rigorous analytical solutions that shape how Aftershoot grows and retains its global user base. You'll own the full lifecycle: from scoping the right problem, to building production-grade models, to driving alignment with cross-functional stakeholders.

Problem Scoping & Analytics

- Independently translate business questions from Product, Marketing, and Growth into well-defined analytical problem statements - with minimal rework or scope creep.

- Own the end-to-end analytics lifecycle: from understanding the question to delivering an actionable recommendation.

- Build scenario planning models and strategic analyses that directly inform product roadmap and commercial decisions (e.g., repricing segmentation, churn strategy, seasonality planning).

Model Development & Deployment

- Build and deploy production-grade predictive models - such as churn probability scores at a user × month level - that are activated directly by downstream teams (e.g., Marketing re-engagement campaigns).

- Develop likelihood models for key conversion events (e.g., Trial Conversion Likelihood / Trial Quality Index) using behavioural signals, engagement patterns, and user context.

- Build marketing spend optimisation models to improve ROI on paid acquisition channels.

Experimentation

- Own and roll out a rigorous A/B testing framework - covering experiment documentation, sample sizing, power analysis, guardrail metrics, significance testing, and a centralised results repository.

Communication & Stakeholder Management

- Present complex model outputs, analytical findings, and trade-offs in clear, non-technical language that enables confident decision-making by business stakeholders.

- Embed with OKR planning cycles and task prioritisation across Product, Marketing, and Growth to ensure data bandwidth is aligned with the highest-impact priorities.

- Manage stakeholder expectations on deliverables, timelines, and model limitations - and drive projects through to organisational closure.

- 3+ years of hands-on experience in data science, analytics, or a closely related role.

- Problem Scoping: Demonstrated ability to independently define analytical problems from ambiguous briefs — not just execute on pre-specified tasks.

- SQL (Advanced): Efficient joins, CTEs, window functions, query optimisation for large datasets.

- Predictive Modelling: Proven experience building and deploying models (classification, regression, survival/churn) in a production environment - not just in notebooks.

- Data Visualisation & Dashboarding: Strong experience building self-serve dashboards in Omni, Tableau, Power BI, Looker, or similar tools.

- Experimentation: Solid grasp of A/B testing methodology - power analysis, statistical significance, guardrail metrics, and experiment design.

- Communication: Ability to translate model outputs and analytical findings into business narratives that stakeholders can act on confidently.

- Analytical Thinking: Rigour in selecting the right methodology, questioning assumptions, and structuring a logical approach before execution.

• Captain: Aditya

• Team Members: Tejas, Hinal and the rest of the Aftershoot team.

1️⃣ Phone Screen – If your profile looks like a fit, our People team will reach out for an intro call.

2️⃣ Team Interview – Chat with one of your future teammates to dive deeper into your experience.

3️⃣ Task Round – Show us how you think and solve a real-world problem.

4️⃣ Bar-Raiser Interview – Meet our leadership team for a final evaluation.

5️⃣ Offer! – If it goes well, we’ll extend a formal offer and welcome you on board.