Deputy General Manager Data Science & Technical Lead

Our employees are our company's greatest asset - they are our real competitive advantage. We possesse immense power of innovation, immagination and a desire to attract and retain the best; provide them with encouragement, stimulus, and make them feel that they are an integral part of the company's mission.

Purpose

Deputy General Manager Data Scientist is an exciting role in the Data Science team within Data Analytics and Process Improvement department. This position is responsible to build state of the art machine learning algorithms, maximizing the impact of distribution solutions in driving enterprise performance benefiting from existing landscape including MDMS, SAP, GIS and legacy systems. Typical initiatives include optimizing network KPIs, accurately forecasting customer demand, using Natural Language Processing (NLP) to glean insight on consumer trends from search data, and making individual assortment recommendations for each of the assets currently part of KE distribution network. The role will work closely with a P&E functional leads such as AMI and GIS along with business stakeholders, addressing both large transformation programs and near-term business problems.

Additionally, this resource is a subject matter expert of data integration solutions and data architecture with strong functional and business knowledge to facilitate and empower distribution function in increasing 11 kV network efficiency and process digitization across KE wide distribution function.

The said position will provide leadership for the Data Science team and will be responsible for providing direction, mentorship, and guidance to a team of data scientists and engineers to guarantee their professional and technical development, as well as to foster continuous improvement of the team’s code, architecture, and agile processes. The position will be contributing towards company’s objective of reliable and safe power supply by overseeing the development of predictive models and shift from reactive to proactive, informed and result-oriented network investments.

Education

Minimum 9-10 years of experience in Business / Domain knowledge, Data Integrators technology landscape with implementation background of utilities and electrical equipment.

Minimum of B.S. in a relevant technical field (e.g. Computer Science, Engineering, Statistics, Operations Research); preferably a postgraduate (Masters or Doctorate) degree

Knowledge

• Machine learning Expert
• Statistical modelling Expert
• Forecasting Expert
• Optimization techniques and tools
• Deep learning (and applications to NLP & Computer Vision)
• Automated Machine Learning platforms
• Machine Learning techniques & algorithms including
• K-NN
• Naïve Bayes
• SVM
• Decision forecasts
• Data science toolkits including R, Weka, Numpy
  
  

Experience

Minimum 8+ years of experience in Business / Domain knowledge, Data Integrators technology landscape with implementation background of utilities and electrical equipment.

At least 4-5 years building data science solutions to solve business problems, preferably in the Utility industry (less experience may be acceptable if balanced by strong post-grad qualifications)

Experience deploying solutions in a modern cloud-based architecture

Experience managing the work of team members and 3rd party resource vendors

Experience presenting insights and influencing decisions of senior non-technical stakeholders

Competencies

Builds High Performance Team > (Value = Respectful / Energized)

DRIVES CHANGE > Value (ENERGIZED)

DRIVES RESULTS > Value (CUSTOMER CENTRIC + ENERGIZED)

INTERNAL & EXTERNAL STAKEHOLDER MANAGEMENT > Value (CUSTOMER CENTRIC + RESPECTFUL)

MENTAL AGILITY > Value (ENERGIZED)

SELF AWARENESS > Value (ACCOUNTABILITY)

Safe

Area Of Responsibilities

1 Predictive and AI modeling (30%)

 Lead the development of complex data sets and predictive models to support key decisions to improve safety and operational efficiency of the 11kv distribution network and assets including transformers, main cables, switch gear etc through implementation of data science and machine learning on datasets maintained in KE systems.

 Lead the development, maintenance and improvisation of Machine Learning and AI models and communicate the results to leadership to demonstrate the value created through the deployment of such models.

2 Business Warehouse rollout in light of network KPIs (30%)

 Device framework to support technology projects in light of departmental goals through consolidation of data in business warehouse, hence creation of data points through creation of new data points along with enhancement of existing data points.

 Smart meters analytics to gain insights on billable, non-billable and loss streams.

 HT comparison with LT to deep dive and investigate problematic areas using slice and dice functionality.

 Facilitate network planning and other business functions through availability of data points by integrating OMS with different SAP modules and GIS.

 Real time fault intimation to minimize losses and maximize customer service through integrating ADMS Scada with SAP BW.

3 Stakeholders and Leadership engagement (15%)

 Collaborate with cross-functional stakeholders including distribution operations, network engineering and performance improvement teams, to complete end-to-end Machine Learning analyses that include business requirements, data gathering, analysis, scalable solutions, and presentations by conducting user sessions.

 Oversee and deliver insightful presentations and actionable recommendations. Educate leaders and other employees on complex analytical findings in basic terms with storytelling and data visualization through periodic user trainings.

4 Evaluation and Improvement of ML methodologies (15%)

 Identify and evaluate technologies and provide strategic inputs to advance the Machine Learning analytics capabilities in context of the 11kV distribution network and related assets through vendor and consultants coordination and projects.

 Compare the findings from the evaluation with industry best practice and develop a plan to adopt within distribution by mapping use cases and engaging vendors for KE requirements

 Implement new mathematical, machine learning, or other methodologies for modeling or analyses through existing data availability in SAP BW.

5 Industry – Academia Collaboration (10%)

 Coordination with academia to explore ML and AI use cases within KE’s distribution network for betterment and longevity of asset life by engaging final year students and subject matter experts.

 Coordination with technology consultants for latest ML and AI applications in the distribution network domain and use case exploration in KE’s distribution network by analyzing state of the art tools.

KE provides equal employment opportunity (EEO) to all persons regardless of age, color, origin, physical or mental disability, race, religion, creed, gender, marital status, status with regard to public assistance or any other characteristic protected by federal, state or local laws.