Ampcus is hiring: Analyst, Data Science in Plano

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team. Job Title: Data Science Location(s): Plano, TX (Hybrid) Summry Current project is wrapping up soon and the manager's team will start working on new project form next year where they will be predicting future expected losses to understand profitability. The manager is looking forward to onboard a Data Science Analyst with 2-3 years of solid experience with SQL and Python coding along with some good experience with Predictive Modelling, GitHub. The candidate should be able to communicate well and should have good analytical skills. Masters in quantitative degree (mathematics, statistics data science, physics, science) will be highly required. What we're looking for This role is ideal for someone who will thrive working at the intersection of computational science, predictive modeling, and business consulting. The ideal candidate will excel at analysis, manipulation, and cleaning of data, building predictive and prescriptive models using a variety of theoretical and computational techniques, extract insights from models to help inform business decisions, and present results and recommendations to various business partners and leaders. Additionally, the ideal candidate will also possess the technical expertise to help transition models from development to production and deploy them efficiently. If you have a passion for end-to-end data solutions and enjoy working in a dynamic, collaborative setting, you could be the perfect fit for our team. What you'll be doing Work closely with team members and business partners to identify and prioritize key questions and drive impactful data-based decisions. Extract, manipulate, and clean data from diverse sources using SQL and Python, preparing it for detailed analysis and model development. Develop and implement sophisticated predictive and prescriptive models using statistical and machine learning techniques. Ensure these models address key business challenges and deliver actionable insights. Create clear, compelling data visualizations to effectively communicate findings to both technical and non-technical stakeholders. Effectively and efficiently transition models from development to production within the organization's existing cloud ecosystem. Participate actively in project planning and prioritization sessions to ensure data initiatives align with business goals. Stay updated with the latest industry trends and tools and integrate this knowledge to improve methodologies and solutions. Requirements: Qualifications/ What you bring (Must Haves) - Highlight Top 3-5 skills Master's degree or higher in a relevant analytical field. Hands-on experience building and optimizing data solutions using Python. Experience solving problems using a variety of statistical and machine learning techniques. Hands-on experience using statistical or machine learning frameworks to solve a variety of real-world problems (e.g., statsmodels, scikit-learn, PyTorch). Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks. Proactive approach to identifying problems and developing innovative solutions. Added bonus if you have (Preferred): Experience with version control systems such as GitHub, and familiarity with CI/CD practices to streamline model deployment and code management. Hands-on experience with cloud-based machine learning platforms (e.g., AWS SageMaker or Azure ML) to leverage scalable computing resources and tools. Demonstrated ability to lead through influence, effectively navigating and prioritizing complex cross-departmental projects to drive impactful change. Capability to replace and bridge existing legacy infrastructure and processes. Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identify, national origin, age, protected veterans or individuals with disabilities.