Deputy General Manager Data Science & Technical Lead K-Electric

Job title: Deputy General Manager Data Science & Technical Lead Company: K-Electric Job description: Our employees are our company’s greatest asset – they are our real competitive advantage. We possess immense power of innovation, imagination, 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: The Deputy General Manager Data Scientist is an exciting role in the Data Science team within the Data Analytics and Process Improvement department. This position is responsible for building state-of-the-art machine learning algorithms, maximizing the impact of distribution solutions in driving enterprise performance benefiting from existing landscapes including MDMS, SAP, GIS, and legacy systems. Typical initiatives include optimizing network KPIs, accurately forecasting customer demand, using Natural Language Processing (NLP) to glean insights on consumer trends from search data, and making individual assortment recommendations for each of the assets currently part of the KE distribution network. The role will work closely with 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 functions in increasing 11 kV network efficiency and process digitization across the KE-wide distribution function. This 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 contribute towards the company’s objective of reliable and safe power supply by overseeing the development of predictive models and shifting 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 in 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:

1. Machine learning Expert
2. Statistical modelling Expert
3. Forecasting Expert
4. Optimization techniques and tools
5. Deep learning (and applications to NLP & Computer Vision)
6. Automated Machine Learning platforms
7. 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 in 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:

1. Builds High Performance Team (Value = Respectful / Energized)
2. DRIVES CHANGE (Value = ENERGIZED)
3. DRIVES RESULTS (Value = CUSTOMER CENTRIC + ENERGIZED)
4. INTERNAL & EXTERNAL STAKEHOLDER MANAGEMENT (Value = CUSTOMER CENTRIC + RESPECTFUL)
5. MENTAL AGILITY (Value = ENERGIZED)
6. SELF AWARENESS (Value = ACCOUNTABILITY)

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 the business warehouse, hence creation of 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. Expected salary: Location: Karachi, Sindh Job posting date: Sat, 21 Dec 2024 07:06:52 GMT Apply for the job now!#J-18808-Ljbffr