Consultant, Forecasting Models (Consulting + Data Science)

Sybilion
07, January 2026
IA e Ciência de Dados
Tempo inteiro

Descrição do trabalho

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
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