Principal Data Scientist

The Principal Data Scientist applies strong expertise in artificial intelligence through use of machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engines and services. The Principal Data Scientist collaborates with business partners to define the technical problem statement and hypothesis to test; develops efficient and accurate analytical models that mimic business decisions and incorporates them into analytical data products or tools with the support of a cross-functional team. He/ she is also responsible for defining and implementing DS standards and toolkit within EGA and building and nurturing the Data Science community within and beyond the CoE.

KEY ACCOUNTABILITIES:

1. Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
2. Advocate and educate on the value of data-driven decision making focusing on the “how and why” of solving problems.
3. Lead analytic approaches, integrating work into applications and tools with data engineers, business leads, analysts, and developers.
4. Expert in developing data science capability and can advocate the importance of continuous learning to the data science team. Knows how to develop data science capability across the wider organization and in industry. Sets as a role model of best practice through demonstration of his own self-directed learning. You set the data science curriculum across the organization.
5. Create repeatable, interpretable, dynamic, and scalable models that are seamlessly incorporated into analytic data products.
6. Ensure consistent and high-quality data science standards and tools are deployed and developed within the organization.
7. Engineer features by using business acumen to find new ways to combine disparate internal and external data sources.
8. Share passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods, and standards.
9. Collaborate, coach, and learn with a growing team of experienced Data Scientists.
10. Provide leadership and direction across a program of multidisciplinary data science projects, managing resources to ensure delivery.
11. Recognized as a strategic authority with technical expertise in cutting-edge techniques, defining vision across the organization.
12. Communicate with senior stakeholders and convince them of the strategic value of applying data science.
13. Stay connected with external sources of ideas through conferences and community engagements.

AUTHORITY/DECISION MAKING:

Thinking within broadly defined policies, standards, and objectives. The determination of “what needs to be done” in applying policies is largely left up to the incumbent who must establish the plan, determine the priorities, and prescribe the processes needed to achieve the objectives. Subject to general direction and broadly defined functional policy objectives. Actions which will impact other functional or operating areas usually require approval before they may be implemented.

QUALIFICATIONS & SKILLS:

Qualifications

1. MS or PhD in Data Science, Computer Science, Engineering, Statistics.
2. Programming languages knowledge, ideally in Python, R, and SQL.
3. 10+ years of relevant experience in a statistical and data science role.
4. Should have domain expertise and knowledge in Metal & Mining or Energy or a combination of both industries and can communicate the business benefit of data science products, championing and governing these across the organization.
5. Practices applied maths, statistics, and scientific practices, knows how to develop deeper expertise on a narrower range of specialisms and can start to apply emerging theory to practical situations.
6. Proficiency working with Data Frames and writing complex SQL queries.
7. Proficiency with Machine Learning to solve clustering, classification, regression, anomaly detection, simulation, and optimization problems on large scale data sets.
8. Proven ability to merge and transform disparate internal & external data sets together to create new features.
9. Experience of visualizing/presenting data for stakeholders using: Tableau, R Shiny, Plotly, Power BI, Periscope, Business Objects, D3, ggplot etc.
10. Practices different programming languages and knows how to develop, code, test, correct, and document simple programs or scripts as part of a multidisciplinary team.
11. Strong knowledge of one or more data science or decision science domains (e.g., explainable artificial intelligence, econometrics, deep learning, Natural Language Processing, time series forecasting, deployment, causal inference, uplift modelling, and/or optimization).
12. Understands and can help teams apply a range of techniques to analyze data and provide insight across the product life cycle (data science).
13. Knowledge of distributed computing or NoSQL technologies is a bonus.
14. Experience in automation & control systems and training in similar fields is valued.

Agile/Digital Experience

1. Experience of working with large datasets relational databases (SQL), simulation/optimization, and distributed computing tools (MapReduce, Hadoop, Hive, Spark, MySQL, H20.ai, Cloud AI platforms, containerization etc.) is desired.
2. Experience with supporting deployment, monitoring, maintenance, and enhancement of models desired.

Individual Skills

1. Exceptional communication and collaboration skills to understand business partner needs and deliver solutions.
2. Bias for action, with ability to deliver outstanding results through task prioritization and time management.
3. Demonstrated ability to efficiently learn and solve new business domains and problems.
4. Number sense; healthy skepticism around validity of data and its inherent biases.
5. Exemplary organizational skills with attention to detail & accuracy.