Data Culture Lead
2 weeks ago
**Key Responsibilities**:
- Partner with client (product and marketing) teams to understand business problems and marketing strategies as well as engaging with IT to unearth trends in behavior.
- Translate open-ended business problems into analytical hypotheses and test the hypotheses leveraging various data sources.
- Work through the phases of a data science project, from defining data requirements to developing AI/ML models or statistical/econometric models.
- Articulate strategic recommendations based on analysis and sound findings.
- Develop visualizations for stakeholders highlighting nuggets of interest.
- Monitor the teams progress towards developing new, innovative and impactful solutions including delivering them.
- Mentor junior-level staff.
- Build and develop client relationships.
**Job Qualifications**:
- Degree preferred in Engineering (electrical, mining, chemical, etc.), Data Science, Statistics, or related field with excellent academic performance.
- Strong passion for Advanced Data Analytics, Machine Learning, Data Science, and Data Management.
- Strong knowledge of technology trends across IT and digital and how they can be applied to companies to address real-world problems and opportunities.
- Undertaking data collection, preprocessing and analysis.
- Building statistical models to address business problems.
- Well-articulated and presentation of information using data visualization techniques.
- Team-oriented and collaborative working style, both with clients and those within the organization.
- Demonstrated ability to build relationships at relevant levels within client organizations.
- Experience in Machine Learning (R, Python, SAS, SPSS), Big Data (Spark, Hadoop, NoSQL Databases, Data Lakes & Platforms), and Cloud Platforms (Azure, Google Cloud, AWS) with exposure to RDBMS tools like SQL, Oracle, and SAP.
- Experience in data preparation, analysis in Hive/Spark or similar big data analytics tool leveraging Databricks.
- Good grasp of machine learning, development cycle & MLOps, such as model development, model deployment, data versioning, etc.
- 2-4 years of professional experience in Resources / Mining.
- Experience extracting, cleaning, and processing sensors data and working with historian systems such as PI.
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