AddressOur client is looking for AVP- Data Science
Exp: 6+yrs
Location: Remote
CTC: 28LPA
Skills: SQL or SAS / Campaign analytics /Excellent comm skills /MS-Excel and MS-PowerPoint / Stakeholder management
Diversity candidates are highly preferred.
JD:
- Possess a minimum of 6+ years of hands-on experience in Analytics utilizing SQL, SAS, Python, Spark, Scala, and Hive Tools, with proficiency in advanced algorithms such as XG Boost, Random Forest, and Neural Networks, and adeptness in managing complex/large datasets.
- Obtain a Machine Learning Certificate (e.g., AWS Certified Machine Learning, SAS- Certified Data Scientist, or similar).
- Demonstrate excellent verbal and written communication skills, essential for effectively conveying technical insights to diverse audiences, including both technical and non-technical stakeholders across all organizational levels.
- Exhibit strategic thinking capabilities, fostering innovation and creativity in problem-solving to achieve business objectives. Possess a deep understanding of business aspects, including financial P&L drivers and credit card dynamics, demonstrated through successful project delivery.
- Showcase strong project management skills, effective communication, multitasking abilities, and adeptness in building and managing relationships. Demonstrate the capacity to manage complexity and handle multiple initiatives within aggressive timelines.
- Proactively lead discussions with business stakeholders, third-party vendors, and partners to design model specifications aligned with Model Risk Management standards and Model Development procedures, ensuring compliance with internal procedures, standards, policies, and external regulatory guidance.
- Stay abreast of the latest developments in statistical modeling, Data Science, machine learning, artificial intelligence, risk technology, and regulatory environments, providing guidance to stakeholders and supporting projects related to root cause analysis, regulatory examinations, and internal audits of the modeling process.
- Conduct comprehensive assessments of all model aspects, including theoretical aspects, design, implementation, data integrity, reliability, sufficiency of outcomes analysis, and ongoing monitoring effectiveness. Continuously drive better model risk management practices to enhance business efficiency, strengthen controls, improve processes, and foster strong partnerships with model stakeholders.
Proficiently extract and manipulate datasets from source systems using SQL, Python Spark, SAS, etc., effectively manage projects and teams, and engage proactively with stakeholders to build strong relationships across teams, driving business impact and promoting the value proposition of the team.
