Lead Data Scientist

The Lead Data Scientist applies strong expertise in machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engine and services. The Lead Data Scientist collaborates with translators to define technical problem statements and hypotheses to test; develops efficient and accurate analytical models that mimic business decisions.

KEY ACCOUNTABILITIES:

1. Design, create, test and implement complex models and algorithms that drive analytical solutions throughout the organization.
2. Conduct advanced statistical and other analyses to provide actionable insights, identify trends, and measure performance.
3. Collaborate with translators to understand business problems and implement scalable and sustainable solutions.
4. Coordinate with Data Engineers to deliver holistic analytical solutions.
5. Support translators in communicating the design, functioning, and output of the analytical models/solutions developed.
6. Ensure timely analysis and testing for regular maintenance of solutions over time.
7. Lead cross-functional projects using advanced data modeling and analysis techniques to discover insights that will guide strategic decisions and uncover optimization opportunities.
8. Build, develop and maintain data models, reporting systems, data automation systems, dashboards, and performance metrics that support key business decisions.
9. Develop and deliver data science courses as part of the advanced analytics and digital academy.
10. Describe analytic processes from start to finish in the area of expertise.
11. Utilize a variety of data mining/data analysis methods, tools, and algorithms to build and implement models and simulations.
12. Develop processes and tools to monitor and analyze model performance and data accuracy.
13. Perform other duties as assigned.

QUALIFICATIONS & SKILLS:

Minimum Qualifications:

1. BSc or preferably MS/PhD in Computer Science, Electronics, Computer Applications, Statistics, Mathematics, or a related technical discipline.
2. Minimum programming language knowledge in Python, R, Java, and SQL.

Minimum Experience:

1. 5+ years of relevant experience in data science, statistical, and business intelligence roles.
2. Domain knowledge of metal and mining, energy, heavy industry, or oil and gas, or a combination of these industries.
3. Experience in machine learning, statistics, optimization, or related fields; deeper experience in working with large data sets, simulation/optimization, or distributed computing tools is a plus.
4. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, etc.) and experience with applications.
5. Experience working with large data sets and relational databases (SQL), simulation/optimization, and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.) is required.
6. Experience visualizing/presenting data for stakeholders using tools like Periscope, Business Objects, D3, Power BI, Tableau, etc. is required.
7. Knowledge of additional programming languages is a plus (C++, Java, SPSS, SAS, Scala).
8. Working knowledge of data mining principles: predictive analytics, mapping, collecting data from multiple data systems on premises and cloud-based data sources.
9. Data treatment/Data mining experience, e.g., SQL, AWK, Access, Spark, Excel is required.
10. 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 modeling, and/or optimization).
11. Knowledge of distributed computing or NoSQL technologies is a bonus.
12. Experience working with and creating data architectures.
13. Experience in automation & control systems and training in similar fields is valued.
14. Good working knowledge of industry 4.0 applications, big data, autonomous robots, system integration, cloud computing, additive manufacturing, augmented reality, etc.

Agile/Digital skills:

1. Deep knowledge of machine learning models/techniques (including setup, structure, run, stabilize, and implement models).
2. Strong understanding of Agile methodologies.
3. Openness to working in Agile environments with multiple stakeholders.