Senior Applied Data Scientist, Fleet Intelligence

Jobtailor; Deutschland; Remote; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Jobtailor is seeking an experienced data scientist to advance fleet intelligence through predictive analytics and production-ready ML in Germany. The role focuses on developing models for usage, maintenance, and asset economics, translating questions into rigorous hypotheses, and delivering measurable business impact.

You will collaborate with Product, Design, and Engineering to make model outputs explainable and actionable within customer workflows, while establishing robust experimentation and

Qualifikationen

• 5+ years of experience in applied data science or related role.
• Proven track record shipping models that impact outcomes.
• Strong Python and SQL for analysis and model development.
• Solid grounding in statistics and ML fundamentals.
• Experience with forecasting, regression, classification, or similar.
• Experience deploying models into production with monitoring.
• Familiarity with cloud data platforms and Snowflake/dbt.
• Ability to explain complex methods to non-specialists.
• Collaboration with Product, Design, Software Eng, and Data Eng.

Aufgaben

• Deliver near-term Fleet Intelligence initiatives and predictive analytics.
• Develop projections for fleet usage and costs.
• Translate product questions into hypotheses and milestones.
• Explore data to identify predictive signals and gaps.
• Build, validate, and deploy models to production.
• Define model-quality metrics and drift detection.
• Partner with Product and Design to explain outputs.
• Establish reusable experimentation and validation practices.
• Communicate findings to technical and business stakeholders.
• Share knowledge through design reviews and mentorship.

Kenntnisse

Python

SQL

Time-Series Forecasting

Statistical Modeling

Model Deployment

Feature Engineering

Uncertainty Analysis

Tools

Snowflake

dbt

Orchestration Tools

Jobbeschreibung

• Deliver near-term Fleet Intelligence initiatives, including tire intelligence, utilization intelligence, ROI measurement, existing predictive models, and analytical foundations for customer-facing intelligence


• Evaluate and develop projections or predictive models for fleet usage, maintenance cost, availability, condition and failure risk, and asset lifecycle decisions


• Translate product questions into hypotheses, target variables, baselines, evaluation plans, and incremental delivery milestones


• Explore maintenance, usage, cost, work-order, telematics, warranty, and asset-history data to identify predictive signals and data gaps


• Build, validate, and operationalize models from experimentation through production monitoring and iteration


• Define model-quality metrics, confidence thresholds, drift detection, and feedback loops


• Partner with Product and Design to make model outputs understandable, explainable, and actionable in customer workflows


• Establish reusable practices for experimentation, model documentation, validation, monitoring, and responsible predictive-performance claims


• Communicate findings, tradeoffs, risks, and recommendations to technical partners, product leaders, and executives


• Share knowledge through design reviews, documentation, pairing, and mentorship

Requirements

• 5+ years of experience in applied data science, machine learning, statistical modeling, or a closely related role


• Track record of developing and shipping models or decision-support systems that influenced real customer or business outcomes


• Strong proficiency with Python and SQL, including exploratory analysis, feature engineering, model development, and evaluation on large datasets


• Strong grounding in statistics and machine learning fundamentals, including model selection, validation, calibration, uncertainty, bias, and error analysis


• Experience with time-series forecasting, regression, classification, ranking, anomaly detection, survival or reliability analysis, or optimization; depth in several areas more important than breadth across all


• Experience taking models beyond notebooks into reliable production workflows, including versioning, testing, deployment, observability, performance monitoring, and retraining or refresh strategies


• Experience using modern cloud data platforms and transformation workflows such as Snowflake, dbt, and orchestration tools


• Ability to identify data-quality limitations, recommend improvements, and collaborate with data engineers on pipelines and source reliability


• Excellent written and verbal communication, particularly when explaining complex methods, uncertainty, and tradeoffs to non-specialists


• Experience working cross-functionally with Product, Design, Software Engineering, and Data Engineering

Core Competencies

Demonstrates expertise in applied data science and machine learning, with a strong focus on model development, evaluation, and operationalization. Proficient in Python and SQL, capable of translating complex data insights into actionable strategies for fleet intelligence and predictive analytics.

Highest-signal resume keywords

• Applied Data Science


• Machine Learning


• Python Proficiency


• SQL Proficiency


• Predictive Modeling

ATS Optimization Keywords

Hard Skills

• Statistical Modeling


• Feature Engineering


• Time-Series Forecasting


• Regression Analysis


• Classification


• Anomaly Detection


• Model Validation


• Performance Monitoring


• Data Quality Assessment


• Exploratory Data Analysis

Soft Skills

• Excellent Communication


• Cross-Functional Collaboration


• Mentorship

Industry Keywords

• Fleet Intelligence


• Predictive Models


• Data Engineering


• Customer Workflows


• Model Documentation

Tools & Technologies

• Snowflake


• Dbt


• Orchestration Tools