Consultant, Forecasting Models (Consulting + Data Science)

Consultant, Forecasting Models (Consulting + Data Science)
Porto
Porto, Porto District, Portugal

Sybilion builds AI-driven market forecasting for process industries (chemicals, packaging, pulp & paper, textiles, broader manufacturing). We help procurement,
- chain, and commercial teams make confident buy/sell decisions with clear, defensible forecasting signals.

We’re hiring a
- focused Consultant who combines consulting rigor with
- on data science. You’ll be deeply involved in forecast design, validation, and deployment into client workflows, while also supporting discovery, Po
Cs, and executive communication. You’ll work closely with the founders in a
- 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/s
  MAPE/WAPE, bias, coverage, confidence bands, error by horizon/segment).

Operationalise models into decision workflows

• Convert model outputs into
  - ready artefacts: recommended actions, risk flags, thresholds,
  - 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 Po
Cs and client delivery

• Partner with sales on discovery to frame forecasting hypotheses, required data, and what “success” looks like.
• Lead Po
  Cs
  -
  - 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
- 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
  - offs clearly: accuracy vs interpretability, stability vs responsiveness, model complexity vs maintainability.

You’re credible with enterprise clients

• Polished, reliable, precise, high
  - through — our clients are conservative and
  - 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
  - facing data/analytics consulting where modelling was central.
• Strong practical Python (pandas/Num
  Py; notebooks; building/adjusting modelling pipelines; comfortable with
  - 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/Big
  Query; 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 Po
  C 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).