Lendmark Financial Services is looking to solve the problem of data-driven transformation by designing, developing, and deploying advanced analytics solutions that support enterprise-wide initiatives in analytics, automation, and data governance.
Requirements
- Proficiency in Object Oriented Python, R, SQL, and cloud platforms (GCP, AWS, Azure).
- Familiarity with Development Operations frameworks such as Git.
- Experience with machine learning frameworks (TensorFlow, PyTorch, SciKit Learn, Stats Models, etc.).
- Experience with big data tools (PySpark, Hadoop, Kafka).
- Familiarity with data governance, compliance, and organizational design principles.
- Experience in financial services or operational analytics.
- Demonstrated ability to lead cross-functional projects and mentor junior analysts.
Responsibilities
- Design and implement machine learning models and predictive analytics to support business strategy and operational improvements.
- Build and maintain robust data pipelines and scalable data infrastructure using tools such as, SQL, Python, Spark, and MS Azure Fabric.
- Collaborate with cross-functional teams (Operations, Risk, Finance, IT, Marketing) to translate business needs into actionable data solutions.
- Develop dashboards and visualizations using Power BI, Tableau, or similar tools to communicate insights to stakeholders.
- Ensure data integrity, security, and compliance with internal governance protocols.
- Support data storytelling by preparing clear summaries and visualizations that help translate analytical findings into actionable insights.
- Engage in data science initiatives by contributing to coordination efforts, participating in team learning sessions, and assisting with tracking progress toward defined goals.
Other
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- 5+ years of experience in data science and/or data engineering roles.
- Strong communication and stakeholder engagement skills.
- Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- Normal office conditions
