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Lead Data Scientist - Intelligent Forecast Application

Hakkoda
Full-time
Remote
United States
Data Science and Analytics
About Hakkoda 

Hakkoda, an IBM Company, is a modern data consultancy that empowers data driven organizations to realize the full value of the Snowflake Data Cloud. We provide consulting and managed services in data architecture, data engineering, analytics and data science. We are renowned for bringing our clients deep expertise, being easy to work with, and being  an amazing place to work! We are looking for curious and creative individuals who want to be part of a fast-paced, dynamic environment, where everyone’s input and efforts are valued. We hire outstanding individuals and give them the opportunity to thrive in a collaborative atmosphere that values learning, growth, and hard work. Our team is distributed across North America, Latin America, India and Europe. If you have the desire to be a part of an exciting, challenging, and rapidly-growing Snowflake consulting services company, and if you are passionate about making a difference in this world, we would love to talk to you!.

We are seeking an exceptional and highly motivated Lead Data Scientist with a PhD in Data Science, Computer Science, Applied Mathematics, Statistics, or a closely related quantitative field, to spearhead the design, development, and deployment of an automotive OEM’s next-generation Intelligent Forecast Application. This pivotal role will leverage cutting-edge machine learning, deep learning, and statistical modeling techniques to build a robust, scalable, and accurate forecasting system crucial for strategic decision-decision-making across the automotive value chain, including demand planning, production scheduling, inventory optimization, predictive maintenance, and new product introduction. The successful candidate will be a recognized expert in advanced forecasting methodologies, possess a strong foundation in data engineering and MLOps principles, and demonstrate a proven ability to translate complex research into tangible, production-ready applications within a dynamic industrial environment.
This role demands not only deep technical expertise but also a visionary approach to leveraging data and AI to drive significant business impact for a leading automotive OEM.

This role offers an unparalleled opportunity to make a significant impact on the strategic direction and operational efficiency of a global automotive leader, contributing directly to the future of intelligent manufacturing and supply chain management.

Key Responsibilities:

    • Lead the end-to-end design and architecture of the Intelligent Forecast Application, defining its capabilities, modularity, and integration points with existing enterprise systems (e.g., ERP, SCM, CRM).
    • Develop a strategic roadmap for forecasting capabilities, identifying opportunities for innovation and the adoption of emerging AI/ML techniques (e.g., generative AI for scenario planning, reinforcement learning for dynamic optimization).
    • Translate complex business requirements and automotive industry challenges into well-defined data science problems and technical specifications.
    • Design, develop, and validate highly accurate and robust forecasting models using a variety of advanced techniques, including: Time Series Analysis: ARIMA, SARIMA, Prophet, Exponential Smoothing, State-space models. Machine Learning: Gradient Boosting (XGBoost, LightGBM), Random Forests, Support Vector Machines. Deep Learning: LSTMs, GRUs, Transformers, and other neural network architectures for complex sequential data.
    • Probabilistic Forecasting: Quantile regression, Bayesian methods to capture uncertainty.
    • Managing forecasts across multiple product hierarchies, regions, and dealerships. Incorporate diverse data sources, including historical sales, market trends, economic indicators, competitor data, internal operational data (e.g., production schedules, supply chain disruptions), external events, and unstructured data.
    • Conduct extensive exploratory data analysis (EDA) to identify patterns, anomalies, and key features influencing automotive forecasts. |
    • Stay abreast of the latest academic research and industry advancements in forecasting, machine learning, and AI, actively evaluating and advocating for their practical application within the OEM.
    • Architect and implement scalable data pipelines for ingestion, cleaning, transformation, and feature engineering of large, complex automotive datasets.
    • Develop robust and efficient code for model training, inference, and deployment within a production environment.
    • Implement MLOps best practices for model versioning, monitoring, retraining, and performance management to ensure the continuous accuracy and reliability of the forecasting application.
    • Collaborate closely with Data Engineering, Software Development, and IT Operations teams to ensure seamless integration, deployment, and maintenance of the application.
    • Define and implement rigorous evaluation metrics for forecasting accuracy (e.g., MAE, RMSE, MAPE, sMAPE, wMAPE, Pinball Loss) and business impact.
    • Perform A/B testing and comparative analyses of different models and approaches to continuously improve forecasting performance.
    • Identify and mitigate sources of bias and uncertainty in forecasting models.
    • Work cross-functionally with various business units (e.g., Sales, Marketing, Supply Chain, Manufacturing, Finance, Product Development) to understand their forecasting needs and integrate solutions.
    • Communicate complex technical concepts and model insights clearly and concisely to both technical and non-technical stakeholders.
    • Provide technical leadership and mentorship to junior data scientists and engineers, fostering a culture of innovation and continuous learning.
    • Potentially contribute to intellectual property (patents) and present findings at internal and external conferences.

Required Qualifications:

    • Requires PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field.
    • 5+ years of hands-on experience in a Data Scientist or Machine Learning Engineer role, with a significant focus on developing and deploying advanced forecasting solutions in a production environment.
    • Demonstrated experience designing and developing intelligent applications, not just isolated models.
    • Experience in the automotive industry or a similar complex manufacturing/supply chain environment is highly desirable.
    • Expert proficiency in Python (Numpy, Pandas, Scikit-learn, Statsmodels) and/or R.
    • Strong proficiency in SQL. Machine Learning/Deep Learning Frameworks.
    • Extensive experience with TensorFlow, PyTorch, Keras, or similar deep learning libraries.
    • Forecasting Specific Libraries: Proficiency with forecasting libraries like Prophet, Statsmodels, or specialized time series packages. Data Warehousing & Big Data Technologies.
    • Experience with distributed computing frameworks (e.g., Apache Spark, Hadoop) and data storage solutions (e.g., Snowflake, Databricks, S3, ADLS).
    • Hands-on experience with at least one major cloud provider (Azure, AWS, GCP) for data science and ML deployments.
    • Understanding and practical experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines).
    • Proficiency with tools like Tableau, Power BI, or similar for creating compelling data stories and dashboards.
    • Deep understanding of statistical inference, experimental design, causal inference, and the mathematical foundations of machine learning algorithms.
    • Proven ability to analyze complex, ambiguous problems, break them down into manageable components, and devise innovative solutions.

Preferred Qualifications:

    • Publications in top-tier conferences or journals related to forecasting, time series analysis, or applied machine learning.
    • Experience with real-time forecasting systems or streaming data analytics.
    • Familiarity with specific automotive data types (e.g., telematics, vehicle sensor data, dealership data, market sentiment).
    • Experience with distributed version control systems (e.g., Git).
    • Knowledge of agile development methodologies.

Soft Skills:

    • Ability to articulate complex technical concepts and insights to a diverse audience, including senior management and non-technical stakeholders.
    • Strong interpersonal skills and a proven ability to work effectively within cross-functional teams.
    • A passion for continuous learning, staying ahead of industry trends, and proactively identifying opportunities for improvement.
    • Ability to see the big picture and align technical solutions with overall business objectives.
    • Desire and ability to guide and develop less experienced team members.
    • Thrive in a fast-paced, evolving environment with complex challenges.

Benefits:

- Medical, Dental, Vision.
- Life Insurance.
- Paid parental leave.
- Flexible PTO Options.
- Company Bonus Program.
- Work from home benefits.
- Technical training and certifications.
- Robust learning and development opportunities
- Trip to Costa Rica.

Hakkoda is committed to fostering diversity, equity, and inclusion within our teams. A diverse workforce enhances our ability to serve clients and enriches our culture. We encourage candidates of all races, genders, sexual orientations, abilities, and experiences to apply, creating a workplace where everyone can succeed and thrive.

Ready to take your career to the next level? 🚀 💻 Apply today👇 and join a team that’s shaping the future!!

Hakkoda is an IBM subsidiary which has been acquired by IBM and will be integrated in the IBM organization. Hakkoda will be the hiring entity. By Proceeding with this application, you understand that Hakkoda will share your personal information with other IBM subsidiaries involved in your recruitment process, wherever these are located. More information on how IBM protects your personal information, including the safeguards in case of cross-border data transfer, are available here.
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