Data Scientist Senior

<h3>Accountabilities:</h3><ul>
<li>Analyze large and complex datasets to solve unstructured business problems using statistical analysis, econometrics, and machine learning techniques.</li>
<li>Source, ingest, clean, transform, and prepare data for analysis while supporting reliable, scalable, and high-quality data pipelines.</li>
<li>Design, develop, validate, and implement advanced analytical models, including classification, clustering, pattern analysis, sampling, simulations, and predictive modeling.</li>
<li>Build champion/challenger models and continuously refine model performance based on accuracy, reliability, stability, and business feedback.</li>
<li>Develop self-healing model frameworks and support the deployment and productionization of machine learning models in enterprise environments.</li>
<li>Apply statistical and data engineering techniques using SQL, Python, Alteryx, Tableau, Power BI, and related analytical tools.</li>
<li>Create model outputs, visualizations, reports, and educational materials that clearly communicate analytical findings and business value to stakeholders.</li>
<li>Collaborate with senior data scientists, business partners, and technical teams to establish research approaches and tailor analytical methods to business and client requirements.</li>
<li>Review code for accuracy, efficiency, maintainability, quality, and compliance with development best practices.</li>
<li>Provide technical guidance and mentorship to less experienced team members, helping them develop reliable, consumable data products and model outputs.</li>
<li>Participate in stakeholder discussions to explain results, identify analytical opportunities, address concerns, and resolve production challenges.</li>
<li>Monitor data quality, model stability, and scalability across on-premises and cloud-based environments.</li>
<li>Identify and escalate risk-related issues while adhering to applicable regulatory requirements, audit standards, internal controls, and risk management policies.</li>
<h2>Requirements:</h2>
<ul>
<li>Bachelor's degree and at least five years of relevant professional experience, or a combination of higher education and work experience totaling at least nine years, including a minimum of five years of related experience.</li>
<li>At least five years of experience in data science, statistics, econometrics, quantitative analysis, or a related discipline.</li>
<li>Demonstrated experience working with large, complex, and diverse datasets.</li>
<li>Strong understanding of statistical and data science principles, including A/B testing, sample selection, hypothesis testing, model validation, and bias analysis.</li>
<li>Proficiency in statistical software, programming languages, and analytical tools used for data exploration, modeling, and reporting.</li>
<li>Intermediate knowledge of SQL and NoSQL databases, with practical experience performing data extraction, transformation, and loading (ETL) using SQL and Python.</li>
<li>Experience working with hybrid database environments spanning on-premises infrastructure and cloud platforms.</li>
<li>Familiarity with advanced modeling methods, including Bayesian modeling, classification, clustering, neural networks, non-parametric techniques, and multivariate statistics.</li>
<li>Experience creating data visualizations and communicating analytical insights using Tableau and Power BI.</li>
<li>Hands-on experience with Alteryx.</li>
<li>Excellent written and verbal English communication skills, with the ability to explain technical findings clearly and collaborate professionally with diverse stakeholders.</li>
<li>Strong analytical thinking, problem-solving, attention to detail, and the ability to work independently on complex initiatives.</li>
<li>
<strong>Preferred qualifications:</strong>A master's degree or doctorate in Statistics, Economics, Finance, or another quantitative discipline; advanced knowledge of econometric methods such as time-series analysis, panel data techniques, and logistic regression; and experience developing and deploying machine learning models in enterprise production environments.</li>
<h2>Benefits:</h2>
<ul>
<li>Expected base salary of<strong>$43.06–$71.76 per hour</strong>, depending on relevant skills, training, experience, education, market factors, and applicable licenses or certifications.</li>
<li>Competitive benefits package in addition to base compensation.</li>
<li>Fully remote work arrangement.</li>
<li>Six-month contract engagement.</li>
<li>Opportunity to work on complex data science and machine learning initiatives using modern analytical tools and technologies.</li>
<li>Opportunities to provide technical mentorship and collaborate with experienced data science, business, and technology professionals.</li>
<li>Equal employment opportunity in accordance with applicable laws and regulations.</li>
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