Job
Description
What does a successful Statistical Analysis do at Fiserv? Fiserv is looking for a talented analytical who plays a crucial role in helping clients make informed decisions based on data-driven insights. Fiserv offers a range of solutions for financial institutions, businesses, and merchants, analytics lead contributes to the success of these solutions by providing expert guidance on utilizing data effectively. Using product knowledge, subject matter expertise and technical skills, he will provide the highest level of service to resolve the client/customer's issues. What you will do: Create comprehensive strategies to proactively identify and mitigate potential challenges and opportunities. Collaborate with cross-functional teams to ensure alignment with company objectives. Oversee and analyze large datasets to identify patterns, trends, and anomalies. Utilize statistical modeling, machine learning algorithms, and data visualization tools to gain actionable insights and drive data-driven decision-making. Evaluate and optimize systems and technologies. Implement improvements to enhance the accuracy and efficiency of data analysis. Develop and maintain regular reports on key metrics, trends, and insights. Present findings to stakeholders, recommending appropriate actions based on analysis. Stay abreast of the latest trends, tools, and technologies in your field. Identify opportunities to enhance existing processes and methodologies, driving continuous improvement within the team. Ensure Compliance and Best Practices: Ensure that all activities align with industry regulations and company policies. Collaborate with relevant teams to address any potential issues. What you will need to have: Bachelors or master’s degree in a relevant field such as Data Science, Statistics, Computer Science, or a related discipline. Proven experience (typically 6-11+ years) in analytics. Strong analytical skills with expertise in data manipulation, statistical analysis, and machine learning techniques. Proficiency in programming languages such as Python, R, or SQL. Experience with data visualization tools (e.g., Tableau, Power BI) for presenting insights to stakeholders. Excellent leadership and communication skills, with the ability to inspire and motivate a team. What would be great to have: Proficiency in programming languages such as Python, R, or SAS, and experience with relevant libraries and frameworks (e.g., TensorFlow, Py-Torch, scikit-learn). Solid understanding of hypothesis testing, and validation methodologies. Experience with data manipulation, feature engineering, and data preprocessing techniques. Excellent communication skills, with the ability to convey technical concepts to non-technical stakeholders effectively. Experience with data visualization tools and techniques to present validation results in a clear and meaningful way.
