Junior Machine Learning Engineer

Quantiphi

full-time

Posted on: 10/18/2025

Location Type: Remote

Location: Remote • Texas • 🇺🇸 United States

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Junior

BigQueryGoogle Cloud PlatformNumpyPandasPythonScikit-LearnSQLTableau

About the role

Lead the end-to-end process of churn analysis, from data extraction and cleaning to model development and deployment.
Develop and implement advanced statistical and machine learning models (e.g., survival analysis, classification models, time-series analysis) to predict customer churn with high accuracy.
Identify and analyze key churn indicators, patterns, and segments across various customer touchpoints and product usage data.
Conduct deep-dive analyses to uncover root causes of churn and identify actionable insights.
Translate analytical insights into concrete, data-driven retention strategies and actionable recommendations for product, marketing, sales, and customer success teams.
Design, implement, and analyze A/B tests and experiments for various retention initiatives (e.g., personalized communications, feature adoption campaigns, win-back programs).
Develop and maintain comprehensive dashboards and reports to track key churn and retention metrics, providing clear visibility into performance and trends.
Communicate complex analytical findings and strategic recommendations clearly and concisely to both technical and non-technical audiences through compelling data visualizations and presentations.
Partner closely with Business owners and Product Managers to identify product-led retention opportunities and inform roadmap decisions.

Bachelor's or Master's degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Economics, or a related discipline.
2-3 years of progressive experience in data analysis, business intelligence, or data science roles, with a strong focus on customer churn analysis and retention strategies.
Proficiency in Data Analysis: Expert-level SQL skills for querying and manipulating large datasets.
Strong understanding and practical application of statistical modeling, hypothesis testing, regression analysis, time-series analysis, and various machine learning algorithms (e.g., Logistic Regression, Random Forests, Gradient Boosting, Survival Analysis) for predictive modeling.
Advanced proficiency in Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) or R for data manipulation, statistical analysis, and model development.
Expertise in data visualization tools such as Tableau, Looker, Power BI, or similar platforms to create insightful dashboards and reports.
Hands-on experience with GCP services, particularly BigQuery for data warehousing and analytics.
Demonstrated ability to translate complex analytical findings into clear, actionable business recommendations that drive measurable results.
Strong understanding of customer lifecycle management, customer segmentation, and key business metrics (e.g., LTV, CAC, ARPU).

Benefits

Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
Stay ahead of the curve—immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !

Tip: use these terms in your resume and cover letter to boost ATS matches.

data analysisstatistical modelinghypothesis testingregression analysistime-series analysismachine learningSQLPythonRdata visualization

communicationanalytical thinkingproblem-solvingcollaborationpresentation skillsstrategic thinkingactionable insightsdata-driven decision making