Data Scientist - Consumer Anaytics, Forecasting

As a Data Scientist specializing in consumer analytics and forecasting, you will be responsible for delivering complete data science solutions that address real-world business challenges. You will combine analytical expertise, machine learning techniques, and business understanding to generate insights, optimize processes, and support strategic decision-making.

• Manage end-to-end data science initiatives, including business understanding, data preparation, modeling, evaluation, and deployment.


• Analyze complex datasets and interpret findings to identify meaningful business insights.


• Develop predictive models and forecasting solutions to support business planning and operational improvements.


• Translate analytical outcomes into clear recommendations, including expected business benefits and ROI measurement.


• Gather business requirements and convert them into technical plans, analytical approaches, and implementation strategies.


• Perform feature engineering, model evaluation, and optimization to improve model performance.


• Apply machine learning techniques such as decision trees, ensemble models, regression, and other classical ML approaches.


• Support pre-sales activities by contributing technical expertise and presenting data science solutions to stakeholders.


• Collaborate with cross-functional teams to understand customer challenges and develop creative analytical solutions.


• Contribute to best practices in data science methodologies, model validation, and deployment processes.

Requirements:

The ideal candidate is an experienced data scientist with strong machine learning expertise, business acumen, and the ability to independently deliver analytical solutions. You should be comfortable working with customer analytics, forecasting problems, cloud platforms, and modern data science tools.

• Commercial experience applying classical data science and machine learning models, including decision trees, ensemble-based models, and linear regression.


• Strong understanding of consumer analytics concepts and advanced forecasting techniques.


• Experience with hyperparameter tuning and model validation frameworks.


• Ability to gather business requirements, design technical solutions, process data, engineer features, and evaluate models.


• Experience supporting business teams through analytical insights and data-driven recommendations.


• Strong programming skills in Python and basic working knowledge of SQL.


• Familiarity with data science and machine learning libraries.


• Experience working with cloud computing platforms such as Databricks, GCP, or Azure.


• Strong analytical thinking and ability to solve complex business problems creatively.


• Good understanding of programming concepts and software development practices.


• Preferred: Knowledge of causal machine learning and advanced modeling approaches.


• Preferred: Experience working with big data environments and distributed computing.


• Preferred: Experience with forecasting projects, MLOps, object-oriented programming in Python, or additional languages such as R and Scala.


• Preferred: Experience with deep learning, reinforcement learning, or other advanced data science techniques.

Benefits:

• Fully remote work environment with flexibility regarding working hours.


• Stable full-time employment within an established global technology organization.


• Comprehensive onboarding program with dedicated support from day one.


• Opportunity to collaborate with experienced engineers, data scientists, and technology experts.


• Access to online learning platforms and continuous professional development resources.


• Technology certification programs and support for career advancement.


• Capability development programs, knowledge-sharing sessions, and community learning opportunities.


• Strong internal growth opportunities with career progression paths.


• Diverse, inclusive, and values-driven workplace culture.


• High level of autonomy and trust in how you organize your work.


• Employee referral programs and community-building activities.


• Wellness initiatives and opportunities to support health and well-being.


• Opportunities to contribute to charitable and environmental initiatives.