OverviewJoin to apply for the Senior Applied ML Scientist role at hackajob.hackajob is collaborating with Nory to connect them with exceptional tech professionals for this role. Let’s fix hospitality, for good. We’re on a mission to help the hospitality industry double their profitability, reduce their carbon footprint, and create better working environments for their teams. Nory is an all-knowing restaurant management system that combines real‑time data with AI predictive analytics to help restaurants operate with consistency, certainty and profitability. From food prep to forecasting, Nory puts operators in control of their margins.We’re now looking for a Senior Applied ML Scientist to join our Data team. This role is ideal for a senior independent doer — someone who has repeatedly owned ML systems end-to-end, applied strong classical ML/statistical reasoning, and delivered models that make or save money in the real world. This is not a research or experimentation role. It’s about building pragmatic ML systems that drive KPIs - forecasting, optimisation, regression, and classification - and ensuring those systems keep working in production.
Own the full ML lifecycle: from business problem formulation and algorithm design through to deployment, monitoring, and iteration in production
Apply strong classical ML thinking: regression, time series forecasting, classification, optimisation, clustering - picking the right tool and justifying why
Keep models alive: build monitoring into every deployment (drift detection, retraining, KPI tracking) so models keep delivering value in production
Collaborate deeply with squads: work alongside product managers, engineers, and designers to define KPIs, design data flows, and embed ML into product features
Framework in business terms: focus on outcomes (profitability, efficiency, waste reduction), not just technical metrics
Contribute to culture: share learnings, give feedback, and help raise the bar for applied ML at Nory
What you’ll bring
Background: MSc/PhD in a stats-heavy STEM field (Applied Maths, Statistics, Physics, Econometrics)
E2E ownership: 4+ years of experience taking ML systems from idea to production in a commercial setting, independently owning the process from problem formulation to monitoring impact
Classical ML theory depth: time-series forecasting, regression/classification, causal signals, feature engineering, evaluation/monitoring, and drift
Technical Proficiency: Strong Python and ML libraries (e.g., Scikit-Learn, Pandas, LightGBM), plus working knowledge of cloud infrastructure and data tools (Snowflake, DBT, Omni)
Rigour & Impact: Rigorous statistical and experimental thinking with demonstrated business outcomes
Collaboration: Thrive in collaborative environments, communicate clearly, and help improve team outcomes
Startup mindset: Fast-paced, proactive, able to handle ambiguity and multitask while delivering real impact
Nice-to-have
Experience in smaller data teams with high ownership cultures
Exposure to B2B SaaS products
Familiarity with LLMs - useful, but not a focus for this role
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Research, Analyst, and Information Technology
Industries
Software Development
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