Data Science - Causal

HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for Data Science - Causal in Remote. Below is the detailed job description.

Title: Data Science - Causal

Location: Various locations,North America - Remote

Duration: FTE/Contract

As a Data Science Lead, you will manage multiple consulting projects of varying complexity and ensure on-time and on-budget delivery for clients. You will lead a team of data scientists and collaborate across cross-functional groups, while contributing to new business development, supporting strategic business decisions and maintaining & strengthening client base

1. Work with team to define business requirements, come up with analytical solution and deliver the solution with specific focus on Big Picture to drive robustness of the solution

1. Work with teams of smart collaborators. Be responsible for their appraisals and career development.

1. Participate and lead executive presentations with client leadership stakeholders.

1. Be part of an inclusive and open environment. A culture where making mistakes and learning from them is part of life

1. See how your work contributes to building an organization and be able to drive Org level initiatives that will challenge and grow your capabilities.

THE IDEAL CANDIDATE WILL:

1. Serve as expert in Data Science, build framework to develop Production level DS/AI models.

1. Apply AI research and ML models to accelerate business innovation and solve impactful business problems for our clients.

1. Lead multiple teams across clients ensuring quality and timely outcomes on all projects.

1. Lead and manage the onsite-offshore relation, at the same time adding value to the client.

1. Partner with business and technical stakeholders to translate challenging business problems into state-of-the-art data science solutions.

1. Build a winning team focused on client success. Help team members build lasting career in data science and create a constant learning/development environment.

1. Present results, insights, and recommendations to senior management with an emphasis on the business impact.

1. Build engaging rapport with client leadership through relevant conversations and genuine business recommendations that impact the growth and profitability of the organization.

ELIGIBILITY CRITERIA:

• Bachelor's /Master's /PhD degree in a quantitative field (CS, Machine learning, Mathematics, Statistics, Data Science) or equivalent experience.
• 8-14 years of experience in data science, building hands-on ML models
• Expertise in Causal is mandatory.
• Expertise in ML Regression, Classification, Clustering, Time Series Modeling, Graph Network, Recommender System, Bayesian modeling, Deep learning, Computer Vision, NLP/NLU, Reinforcement learning, Federated Learning, Meta Learning.
• Proficient in some or all of the following techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines ANOVA , Principal Component Analysis, Gradient Boosted Trees, ANN, CNN, RNN, Transformers.
• Knowledge of programming languages SQL, Python/ R, Spark.
• Expertise in ML frameworks and libraries (TensorFlow, Keras, PyTorch).
• Experience with cloud computing services (AWS, Google Cloud Platform or Azure)
• Expert in Statistical Modelling & Algorithms E.g. Hypothesis testing, Sample size estimation, A/B testing
• Knowledge in GPU code optimization, Spark MLlib Optimization.
• Familiarity to deploy and monitor ML models in production, delivering data products to end-users.
• Experience with ML CI/CD pipelines.

ABOUT YOU:

• You are self-motivated, collaborative, eager to learn, and hands on
• You love trying out new apps, and find yourself coming up with ideas to improve them
• You stay ahead with all the latest trends and technologies
• You are particular about following industry best practices and have high standards regarding quality.