Remote Data Scientist – United States – Build Scalable ML Solutions

Join Apex Analytics as a Remote Data Scientist and turn complex data into actionable insights that power next‑generation products. You’ll collaborate with cross‑functional teams to design, deploy, and monitor machine‑learning models at scale. This fully remote role offers flexibility, competitive compensation, and a culture of continuous learning.

About the Role

As a Remote Data Scientist at Apex Analytics, you will own the end‑to‑end lifecycle of data‑driven products—from exploratory analysis and feature engineering to model training, validation, and production monitoring. You will work closely with product managers, engineers, and business stakeholders to translate high‑level objectives into measurable ML solutions that drive revenue and improve user experience. The position is 100 % remote, allowing you to work from anywhere in the United States while staying connected through a robust virtual collaboration stack.

Key Responsibilities

• Design and implement predictive models, recommendation engines, and anomaly‑detection systems using Python, SQL, and modern ML frameworks (TensorFlow, PyTorch, scikit‑learn).
• Build reproducible data pipelines and feature stores on cloud platforms (AWS, GCP, or Azure) with tools such as Airflow, dbt, and Delta Lake.
• Conduct rigorous A/B testing and causal inference studies to validate model impact on key business metrics.
• Monitor model performance in production, set up drift detection, and orchestrate automated retraining cycles.
• Communicate findings and technical trade‑offs to non‑technical audiences through clear visualizations and storytelling.

Requirements

• Master’s degree or higher in Computer Science, Statistics, Mathematics, or a related quantitative field (or equivalent practical experience).
• 4+ years of professional experience building and deploying machine‑learning models in a production environment.
• Deep proficiency in Python (pandas, NumPy, SciPy) and SQL; experience with Spark or Dask for large‑scale data processing.
• Strong grasp of statistical modeling, experimental design, and model evaluation techniques.
• Hands‑on experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker) and CI/CD pipelines.
• Excellent written and verbal communication skills; ability to thrive in an asynchronous, distributed team.

Preferred Qualifications

• Publications or open‑source contributions in machine learning, causal inference, or data engineering.
• Experience with real‑time inference systems and low‑latency model serving.
• Familiarity with privacy‑preserving ML (federated learning, differential privacy).
• Background in a high‑growth SaaS, fintech, or e‑commerce environment.

Salary Range

$120,000 – $160,000 USD/year (base salary, commensurate with experience and location within the United States).

Benefits & Perks

• Comprehensive health, dental, and vision insurance (100 % employer‑paid for employee).
• Generous 401(k) matching (up to 5 %).
• Unlimited paid time off plus 10 company‑wide holidays.
• $2,500 annual learning stipend for courses, conferences, or certifications.
• Home‑office setup allowance ($1,500 one‑time) and monthly internet reimbursement.
• Quarterly virtual team‑building events and an annual all‑hands retreat (travel covered).

Culture & Values

Apex Analytics is built on curiosity, transparency, and impact. We encourage experimentation, celebrate data‑driven decision making, and invest heavily in personal growth. Our distributed team spans multiple time zones, yet we stay aligned through weekly syncs, shared OKRs, and a culture of constructive feedback. Diversity of thought is not just welcomed—it’s essential to the innovative solutions we deliver.

How We Work

• Fully remote, asynchronous-first workflow with core collaboration hours (10 am–2 pm ET).
• Two‑week sprint cycles, bi‑weekly retrospectives, and monthly demo days.
• Extensive documentation in Notion, code reviews on GitHub, and automated testing in GitHub Actions.
• Regular “office hours” with senior leadership for mentorship and career planning.