Data Scientist - Hybrid

Data Scientist - Hybrid position 2x/week in Vienna, Virginia office. Our client is seeking a motivated and talented mid-level Data Scientist (3-7 years of experience) to join their growing Digital data science department. The Digital data science department is dedicated to building predictive solutions for mobile applications and online business operations. They develop predictive tools to assist business and app developers in enhancing user experience and optimizing operational performance. This role involves collaborating with senior team members, leaders, and business units to create custom solutions and data science models that provide insights into customer digital behavior, forecast mobile app and website usage trends, assess customer app store sentiment, and analyze data for actionable insights. Knowledge or insights into financial services and banking products will be helpful in designing solutions and predictive tools to help customers improve financial management and financial health/well-being. Responsibilities • Collaborate with senior data scientists and leaders to design and implement predictive models and custom solutions that enhance digital offerings • Partner with business units to gather requirements, understand customer needs, and develop data science solutions that address specific business challenges • Write efficient code in Python and SQL to manipulate and analyze data • Design, develop, and evaluate predictive models and algorithms with moderate to high complexity • Utilize traditional and machine learning techniques and tools to build a variety of models, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting • Analyze and interpret results with some complexity • Exercise limited judgment and discretion within defined procedures and practices • Develop and code modeling programs, algorithms, and automated processes • Use modeling and trend analysis to analyze data • Collaborate with team members and participate in team projects and initiatives • Utilize effective written and verbal communication to document and present findings Qualifications • 3-7 years of experience (mid-level) in data science, statistics, and data analytics • Ability to work with moderate to minimal supervision • Strong programming abilities in SQL, Python, PySpark, R, or similar languages in data exploration, data preparation, modeling, prediction, simulation, and statistical analysis • Experience with machine learning techniques and tools to build a variety of models, including logistic regression, XGBoost, neural networks, NLP, and clustering • Ability to use data and cloud environments such as Azure, Databricks, AWS, or Hadoop • Familiarity with project management tools such as Azure Dev Ops (ADO), or JIRA is a plus • Technical writing skills • Strong communication and data storytelling presentation skills of technical material • Ability to collaborate and build relationships • Required: Bachelor's degree in data science, statistics, economics, mathematics, computer science, engineering, or degrees in similar quantitative fields • Desired: Master's degree in data science, statistics, mathematics, or degrees in similar quantitative fields CC Pace is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, or any other protected characteristic under federal, state, or local laws. CC Pace are committed to employing only candidates who are legally authorized to work in the United States. For us to comply with the Immigration Reform and Control Act of 1986, all new employees, as a condition of employment, must complete the Employment Eligibility Verification Form I-9 and provide documentation that establishes identity and authorization to work. E-Verify will be used for employment verification as part of your onboarding process.