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Data Scientist

Lingaro

Role overview

Qualifications

  • Commercial experience with classical ML models (e.g., decision trees, ensembles, linear/logistic regression).
  • Strong knowledge of customer analytics concepts or advanced forecasting techniques, including hyperparameter tuning and model validation frameworks.
  • Fluency in Python and SQL; experience with common DS/ML libraries; solid cloud platform experience (Databricks, GCP, or Azure).
  • Ability to translate business needs into technical goals, gather requirements, and communicate results to stakeholders; solid feature engineering and model evaluation experience.

Responsibilities

  • End-to-end development of classification and forecasting use cases, including problem framing, data preparation, model development, evaluation, and basic deployment support (e.g., batch scoring, APIs).
  • Data exploration and cleaning; perform Exploratory Data Analysis (EDA) to understand datasets and identify data quality issues; engineer features for tabular and time-series data.
  • Train, validate, and tune standard ML models (e.g., logistic regression, decision trees, ensembles, gradient boosting, classical time-series models, simple neural networks); evaluate models using metrics aligned with business KPIs; build visualizations and present insights to stakeholders.
  • Collaborate with data engineers and AI engineers to bring models to production (batch scoring, APIs, monitoring, dashboards); document data sources, modeling assumptions, and experiment results; translate business needs into technical goals; participate in pre-sales activities for senior consultant level.

Key facts

Hard skills

Cloud Computing Feature Engineering Python (Programming Language) Time Series Time Series Model Validation Forecasting Management Ensemble Methods Computer Programming SQL (Programming Language) Neural Engineering Exploratory Data Analysis Machine Learning Data Cleansing Auditing Documentation Generation Tabular Data Decision Tree Learning Requirements Engineering Customer Analysis Data Libraries Data Preprocessing Logistic Regression Boosting Model Building Distributed Computing Reinforcement Learning Object Model Scala (Programming Language) Deep Learning Forecasting Management R (Programming Language) Sales Engineering Big Data Causal Inference Cross-Functional Collaboration Documentation Generation Data Visualization Stakeholder Communications Integrated Reporting

Other skills

  • Forecasting
  • Communication
  • Creative Problem Solving
  • Business Acumen

About the company

Lingaro logo

Lingaro

Management Consulting

Lingaro Group is the end-to-end data services partner to global brands and enterprises. Our industry-recognized capabilities and expertise: • Supply chain analytics • Commercial analytics • Digital commerce • Sustainability analytics • ALCHEMY. creative agency • Data strategy consulting • AI and machine learning • Data platforms • Data visualization • Data governance We are award-winning and globally recognized. - Lingaro is recognized as a ‘Strong Performer’ in the 2022 Gartner® Peer Insights™ “Voice of the Customer” report in data and analytics. - We earned the Great Place to Work® certification in Lingaro Poland in 2023 and in Lingaro Philippines in 2022 and 2023, reflecting our people's trust in our core values. - Lingaro Group has been recognized as a Star Performer in Everest Group’s 2024 Analytics and AI Services Specialists PEAK Matrix® Assessment and a “Major Contender” by the Everest Group on its 2022 Analytics and AI Services Specialists PEAK Matrix® Assessment. - Lingaro Philippines won the Asian Technology Excellence Award for a pioneering sales intelligence solution that helped a manufacturing company make faster and smarter business decisions, automate many of its business processes, and improve its sales and marketing performance. - Lingaro was named Microsoft Poland’s Azure Partner of the Year in 2018 for our outstanding innovations and technology solutions.

Company details

Company typeLarge
IndustryManagement Consulting
Company size1001 - 5000

Links

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Job description

What You'll Be Doing: 
  • Work on end‑to‑end classification and forecasting use cases, including problem framing, data preparation, model development, evaluation, and basic deployment support (e.g., demand forecasting, churn prediction).
  • Explore and clean data; perform Exploratory Data Analysis (EDA) to understand datasets and identify data quality issues.
  • Engineer features for tabular and time‑series data.Train, validate, and tune standard Machine Learning models (e.g., logistic regression, decision trees, ensemble methods, gradient boosting, classical time‑series models, simple neural networks).
  • Evaluate models using appropriate metrics with clear impact on business KPIs.
  • Build clear visualizations and deliver concise reports to present insights and model results to business stakeholders.Collaborate with data engineers and AI engineers to bring models to production (batch scoring, APIs, monitoring, dashboards).
  • Document data sources, modeling assumptions, and experiment results in a reproducible manner (notebooks, reports, wikis).
  • Translate business needs into technical goals by defining success metrics, auditing data feasibility, and aligning with stakeholder expectations.
  • Participate in pre‑sales activities (for senior consultant level).
What We’re Looking For:  
  • Commercial experience with various classical data science and ML models (e.g., decision trees, ensemble models, linear/logistic regression).
     
  • Strong knowledge of customer analytics concepts or advanced forecasting techniques. Experience in:
    • Hyperparameter tuning
    • Model validation frameworks
    • Requirements gathering and translating business needs into technical plans
    • Feature engineering and model evaluation
  • Previous experience in an analytical role supporting business functions (a plus).
  • Fluency in Python and working knowledge of SQL.
  • Knowledge of common DS/ML libraries.
  • Solid experience with at least one cloud platform: Databricks, GCP, or Azure.
  • Basic computer programming skills and understanding of core programming concepts.
  • Strong business acumen.
  • Experience with advanced modeling techniques such as deep learning or reinforcement learning (a plus).
  • Ability to develop creative solutions to customer challenges.

Nice to have:

  • Understanding of causal machine learning.
  • Experience working with big data and distributed computing environments.
  • Proven commercial experience with successful forecasting projects.
  • Experience with Object-Oriented Programming (OOP) in Python.
  • Experience with MLOps practices and tooling.Familiarity with additional languages such as R or Scala.

 

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Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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