Responsibilities

•Identify valuable data sources and automate collection processes
•Undertake preprocessing of structured and unstructured data
•Analyze large amounts of information to discover trends and patterns
•Build predictive models and machine-learning algorithms
•Combine models through ensemble modeling
•Present information using data visualization techniques
•Propose solutions and strategies to business challenges
•Collaborate with engineering and product development teams
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom machine learning models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
• Coordinate with different functional teams to implement models and work with Machine Learning DevOps to productionalize and monitor outcomes.
• Collecting large sets of structured and unstructured data from disparate sources
• Cleaning and validating the data to ensure accuracy, completeness, and uniformity
• Devising and applying models and algorithms to mine the stores of big data
• Analyzing the data to identify patterns and trends
• Interpreting the data to discover solutions and opportunities
• Communicating findings to stakeholders using visualization and other means
Work closely with business partners and engagement managers to translate complex business problems into analytics problems and solutions. Ask questions to understand business intent, problem statement, analytics opportunity, and value creation.
• Work closely with data engineering team to identify and consume relevant structured and unstructured data sources (including IoT sources such as manufacturing sensors systems).
• Identify key hypotheses and data science approaches to answering analytics problems and getting to business outcomes.
• Develop statistical and machine learning models/algorithms through iterative process and rapid prototyping.

What your background should look like:

• An undergraduate degree or master’s degree in data science, predictive analytics, computer science, applied mathematics, statistics, software engineering, physics, or related quantitative discipline.
• Hands-on experience in machine learning and statistical modelling, including a demonstrated high-level of proficiency in applying data science techniques to solving enterprise problems.
• High proficiency in conducting analyses using tools like Python, R and data visualization tools (e.g., Tableau, Power BI, Qlik, Ploty).
• Rigorous understanding of the fundamentals of statistics, machine learning and artificial intelligence using both structured and unstructured data sets.
• Experience in developing hypotheses or analytics solutions for business problems.
• Experience in presenting complex analytics methodologies, analyses, and insights in simple and concise manner to the business partners and senior leaders.

Educational Skills

• A PhD degree and/or additional relevant industry certifications (in analytics, software platforms, cloud environments, etc.).
• Preferred experience in developing data science solutions in marketing, pricing and/or supply chain domains.