Applied Data Scientist (The Insight Engineer)

Are you passionate about applying data science to solve real-world problems and drive actionable insights? Do you excel at turning complex data into meaningful, business-impactful solutions? If you’re ready to build and deploy data models that make a difference, our client has the perfect role for you. We’re seeking an Applied Data Scientist (aka The Insight Engineer) to develop and implement models that support key business functions, enhance operations, and enable data-driven strategies.

As an Applied Data Scientist at our client, you’ll work closely with data engineers, product managers, and stakeholders to design and deploy predictive models that support various applications, from customer behavior analysis to operational optimization. Your work will be integral to transforming data into tools that solve business challenges and enhance decision-making.

Key Responsibilities:

1. Develop and Deploy Predictive Models:
• Build and deploy machine learning models for various business applications, including customer segmentation, predictive maintenance, and churn analysis. You’ll ensure models are production-ready and scalable.
2. Collaborate with Cross-Functional Teams:
• Partner with product managers, business analysts, and engineering teams to align model development with business objectives. You’ll gather requirements and deliver data solutions that meet end-user needs.
3. Transform Data into Business Insights:
• Conduct exploratory data analysis to uncover trends, patterns, and actionable insights. You’ll leverage these insights to create models that directly support business growth and efficiency.
4. Optimize and Maintain Models:
• Continuously monitor model performance and refine models based on feedback and new data. You’ll implement retraining workflows and adjust parameters to ensure accuracy over time.
5. Utilize Advanced Data Techniques and Tools:
• Use advanced machine learning techniques, such as ensemble methods, time-series analysis, or deep learning, to solve complex business problems. You’ll work with tools like Python, R, and TensorFlow.
6. Create and Present Data Visualizations and Reports:
• Develop visualizations and reports to communicate insights and model performance to non-technical stakeholders. You’ll ensure that findings are accessible and meaningful for decision-makers.
7. Stay Updated on Industry Trends and Techniques:
• Keep current with the latest advancements in data science, machine learning, and AI. You’ll integrate new techniques that can improve model accuracy, scalability, and business relevance.

Required Skills:

• Machine Learning and Statistical Modeling: Expertise in developing and implementing machine learning models, including experience with supervised and unsupervised learning techniques.
• Programming and Data Analysis: Proficiency in Python, R, or SQL for data analysis and model development, along with experience in libraries like Scikit-learn, TensorFlow, or PyTorch.
• Business Acumen: Ability to translate complex data into business insights, with an understanding of key metrics and KPIs that drive value for the organization.
• Data Visualization and Communication: Strong skills in creating data visualizations using tools like Tableau, Power BI, or Matplotlib. You can present insights effectively to both technical and non-technical audiences.
• Collaboration and Problem-Solving: Proven ability to work with cross-functional teams and approach problems with a solution-focused mindset.

Educational Requirements:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. Equivalent experience in data science or applied analytics may be considered.
• Certifications in data science or machine learning (e.g., AWS Certified Machine Learning – Specialty, TensorFlow Developer Certificate) are advantageous.

Experience Requirements:

• 3+ years of experience in data science or machine learning, with a proven record of applying models to solve business challenges.
• Experience deploying models in production environments, ideally with cloud-based platforms (AWS, GCP, Azure).
• Background in a specific industry, such as finance, healthcare, or marketing analytics, is beneficial but not required.

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.