Data Scientist
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.
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.
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.
Apply Now
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.
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.
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.
Apply Now