Data Science Sr Analyst

hace 2 días

Cornellà de Llobregat, España PepsiCo A tiempo completo

Overview:
**Are you ready to drive PepsiCo’s digital evolution and accelerate transformation across our global operations?**

With data deeply embedded in our DNA, **PepsiCo Data & Analytics** transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo’s leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.

The Data Science Pillar in D+Ai will be the organization where Data Scientist and ML Engineers report to in the broader D+Ai Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within D+A Products. And will support “pre-engagement” activities as requested and validated by the prioritization framework of D+Ai.

**Responsibilities**:
**Your day-to-day with us**:

- Project Participation: Contribute to digital projects, collaborating with team members to ensure successful project execution.
- Subject Matter Expertise: Provide technical knowledge and support for one or more digital projects.
- Innovation Contribution: Actively participate in innovation activities, exploring and implementing cutting-edge data science techniques.
- Collaboration with Product Managers and Data Engineers
- Solution Support: Support ML engineers in transitioning developed models into industrialized, scalable solutions ready for production.
- Cross-Functional Coordination: Coordinate work activities with business teams, IT services, and other relevant stakeholders to ensure cohesive project progress and integration.
- Platform Toolset Utilization: Support the adoption and use of the Platform toolset, showcasing 'the art of the possible' through demonstrations to business stakeholders as needed.
- Support Experimentation: Assist in large-scale experimentation, building and validating data-driven models to solve complex business problems.
- Set KPIs and Metrics: Help define key performance indicators (KPIs) and metrics to evaluate the effectiveness of analytics solutions for specific use cases.
- Refine Requirements: Translate business requirements into well-defined modeling problems, ensuring clarity and feasibility for the data science team.
- Influence Through Data: Provide data-based recommendations to influence product teams and drive strategic decision-making.
- Research and Development: Stay current with state-of-the-art methodologies, conducting research to integrate the latest advancements into the team’s work.
- Documentation and Knowledge Transfer: Create comprehensive documentation for learnings and ensure effective knowledge transfer within the team and across the organization.
- Develop Reusable Packages: Develop and maintain reusable packages or libraries to enhance efficiency and standardization in data science processes.

Qualifications:
**What you will need to succeed**:

- **Experience**:

- Minimum of 4 years of experience working in the commercial, insights, revenue management, supply chain, manufacturing, or logistics sectors.
- 4+ years of experience working in a team to deliver production-level analytic solutions.
- Proven experience contributing to data science projects and teams.
- Experience in deploying machine learning models into production environments.
- 4+ years of experience in ETL and/or data wrangling techniques. Fluent in SQL syntax.
- 2+ years of experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. Experience with Deep Learning is a plus.
- 2+ years of experience in developing business problem-related statistical/ML modeling with industry tools, primarily focusing on Python or Scala development.
- Experience with large datasets and data engineering pipelines.
- **Technical Skills**:

- Proficiency in programming languages such as Python, R, or SQL.
- Strong understanding of machine learning algorithms and statistical methods.
- Experience with big data technologies like Spark, Hadoop, or similar frameworks.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with data visualization tools like Tableau, Power BI, or similar.
- Fluent in version control systems like Git. Understanding of Jenkins and Docker are a plus.
- Experience with Agile methodology for teamwork and analytics product creation.
- Experience with Reinforcement Learning, Simulation and Optimization problems, Bayesian methods, Causal inference, NLP, and distributed machine learning is a plus.
- Experience with FAIR data and Responsible AI is a plus.

**What makes us different?**
- Hybrid work model: combination of remote and collaborative office experience to enable innovation
- Entrepreneurial environment in leading international company

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