Analista De Suporte Aplicacional Mid

Sybilion builds AI-driven market forecasting for process industries (chemicals, packaging, pulp & paper, textiles, broader manufacturing). We help procurement, supply-chain, and commercial teams make confident buy/sell decisions with clear, defensible forecasting signals. We're hiring a model-focused Consultant who combines consulting rigor with hands-on data science. You'll be deeply involved in forecast design, validation, and deployment into client workflows, while also supporting discovery, PoCs, and executive communication. You'll work closely with the founders in a high-impact role based in Porto. The mission Turn messy market + operational data into forecasting models clients trust - and translate model outputs into decisions that move cost, inventory, service level, and margin. What you'll do: Build & validate forecasting models Design forecasting approaches for prices, demand, lead times, consumption, inventory risk, and volatility depending on the use case. Run EDA, feature construction, and baseline benchmarking (e.G., naive/seasonal, ETS/ARIMA/Prophet, ML models where appropriate). Own model evaluation and sanity checks: backtesting, leakage checks, regime shifts, outliers, structural breaks, and "does this make business sense?" Define and standardise metrics and reporting (MAPE/sMAPE/WAPE, bias, coverage, confidence bands, error by horizon/segment). Operationalise models into decision workflows Convert model outputs into decision-ready artefacts: recommended actions, risk flags, thresholds, what-changed narratives, and "so what" implications. Help shape model outputs into templates, dashboards, and playbooks used by procurement/S&OP/pricing teams. Improve repeatability: contribute to internal model libraries, notebooks, evaluation harnesses, and delivery templates. Model-led PoCs and client delivery Partner with sales on discovery to frame forecasting hypotheses, required data, and what "success" looks like. Lead PoCs end-to-end: data intake → modelling → backtest → insights → exec readout. Run weekly client cadence: progress, risks, stakeholder alignment, and value tracking. Handle live Q&A with credibility: explain why the model says what it says, where it's uncertain, and what we'll do next. Who we're looking for You're model-strong (not just "data literate") You understand the forecasting problem space (time-series, seasonality, volatility, segmentation, horizons) and can choose sensible approaches. You can explain trade-offs clearly: accuracy vs interpretability, stability vs responsiveness, model complexity vs maintainability. You're credible with enterprise clients Polished, reliable, precise, high follow-through — our clients are conservative and detail-oriented. You can align stakeholders around what the model will (and won't) do, and create champions. Must-haves 3–7 years in management consulting (MBB/Big 4/boutique) or client-facing data/analytics consulting where modelling was central. Strong practical Python (pandas/NumPy; notebooks; building/adjusting modelling pipelines; comfortable with time-series basics). Experience working with forecasting evaluation and backtesting; can diagnose why forecasts fail. Business fluency in procurement, supply chain, pricing, or S&OP (enough to connect model outputs to decisions). Executive communication: can produce clear readouts that defend the model and drive action. English fluency; based in / willing to relocate to Porto (hybrid). Nice to have Process industry exposure: chemicals, packaging, pulp & paper, textiles, or adjacent manufacturing. Forecasting toolset familiarity: ARIMA/ETS/Prophet, gradient boosting, causal/external regressors, hierarchical forecasting. SQL; Snowflake/BigQuery; dbt; Jupyter; basic cloud (AWS/GCP). Strong metrics instincts: bias, calibration/coverage, error decomposition, stability over time. Portuguese, German, or Spanish would be advantageous. Compensation & benefits DOE + equity (relocation support available) Performance bonus Learning budget, modern hardware, conference travel Fast path to increased responsibility (e.G., Engagement Manager / Model Lead) What success looks like (first 3–6 months) You ship at least one PoC where the client trusts the model's logic and adopts outputs in a live cadence. You standardise a repeatable modelling + evaluation workflow (baselines → backtest → reporting). You materially improve forecast quality/consistency in one segment (accuracy and trust).