Lead Data Scientist

Job description:

JOB PURPOSE:

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 statement and hypothesis to test; develops efficient and accurate analytical models that mimic business decisions.

KEY ACCOUNTABILITIES:

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

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:

Minimum Qualifications:

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

Minimum Experience:

• 5+ years relevant experience in data science, statistical, and business intelligence role.
• Domain knowledge of metal and mining, energy, heavy industry, oil and gas, or a combination of these industries.
• 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.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
• Experience working with large data sets relational databases (SQL), simulation/optimization, and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.) is required.
• Experience visualizing/presenting data for stakeholders using Periscope, Business Objects, D3, ggplot, Power BI, Tableau, etc. is required.
• Knowledge of additional programming languages is a plus (C++, Java, SPSS, SAS, Scala).
• Working knowledge of data mining principles: predictive analytics, mapping, collecting data from multiple data systems on-premises and cloud-based data sources.
• Data treatment/Data mining, e.g., SQL, AWK, Access, Spark, Excel is required.
• Understanding of and experience using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analyzing data, drawing conclusions, and developing actionable recommendations for business units.
• 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).
• Knowledge of distributed computing or NoSQL technologies is a bonus.
• Experience working with and creating data architectures.
• Experience in automation & control systems and training in similar fields is valued.
• Good working knowledge of industry 4.0 applications, big data, autonomous robots, system integration, cloud computing, additive manufacturing, augmented reality, etc.

Skills:

Agile/Digital skills:

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

Individual skills:

• Strong analytical and problem-solving skills.
• Professional attitude and service orientation; team player.
• Good verbal and written communication skills.
• Quick learner of business needs and translate them into potential analytics solutions.

Mindset & Behaviors:

• Strong work ethic; ability to work at an abstract level and gain consensus.
• Able to build a sense of trust and rapport that creates a comfortable and effective workplace.
• Attitude to thrive in a fast-paced environment.
• Open-minded to new approaches of learning.