- Strong in problem solving, algorithms and data structures
- Proficient in Python
- Hands on experience in technologies and tools related to any of NLP, Deep learning, Machine learning, Conversational AI
- Experience with knowledge Graph or any graph based system is plus
- Able to train and deploy models
- Broad knowledge of machine learning algorithms and principles
- Performance profiling and Tuning
- Communicate and propose solutions to business challenges
- Familiarity with at least one of the cloud computing infrastructure - GCP/AWS
- Keep abreast with the latest technological advances
- Team mentoring and leadership skills.
About US based AI startup company
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About us:
Hypersonix.ai is disrupting the e-commerce space with AI, ML and advanced decision capabilities to drive real-time business insights. Hypersonix.ai has been built ground up with new age technology to simplify the consumption of data for our customers in various industry verticals.
Roles and Responsibilities:
- Collaborate with cross-functional teams to design, develop, and deploy machine learning models and algorithms in supply chain
- Research, experiment with, and implement state-of-the-art machine learning techniques and frameworks to solve challenging problems.
- Develop and optimize deep learning models for different tasks such as NER, matching, image recognition, and generative content creation.
- Stay up to date with the latest advancements in ML and AI research and integrate them into our projects.
- Collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production environments.
- Document research findings, methodologies, and codebase to facilitate knowledge sharing and team collaboration.
- Troubleshoot and resolve issues in production environments to ensure the seamless operation of our systems.
- Ability to perform root cause analysis of product defects and implement effective solutions.
- Design, code, and maintain parts of the product and drive customer adoption.
- Work on using multiple data science methodologies to solve complex business problems.
Qualification/Requirements:
- Strong problem-solving skills and the ability to work on complex, open-ended challenges.
- Motivated self-starter with a strong work ethic and the ability to work independently and as part of a team.
- Proven experience working on NLP and deep learning with a strong portfolio of projects.
- Strong programming skills in Python and the ability to write efficient and maintainable code.
- Proficiency in machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn.
- Experience with cloud-based AI services and infrastructure (e.g., AWS)
- Must have experience in API development and API integration.
- Experience working in production environment, ensuring system stability and performance.
Requirements
Experience
- 5+ years of professional experience in implementing MLOps framework to scale up ML in production.
- Hands-on experience with Kubernetes, Kubeflow, MLflow, Sagemaker, and other ML model experiment management tools including training, inference, and evaluation.
- Experience in ML model serving (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)
- Proficiency with ML model training frameworks (PyTorch, Pytorch Lightning, Tensorflow, etc.).
- Experience with GPU computing to do data and model training parallelism.
- Solid software engineering skills in developing systems for production.
- Strong expertise in Python.
- Building end-to-end data systems as an ML Engineer, Platform Engineer, or equivalent.
- Experience working with cloud data processing technologies (S3, ECR, Lambda, AWS, Spark, Dask, ElasticSearch, Presto, SQL, etc.).
- Having Geospatial / Remote sensing experience is a plus.
Position: Manager/Head- Data Scientist/Machine Learning Engineer
- Algorithm Development: Develop cutting-edge algorithms and predictive models to enhance customer experience, optimize inventory management, and drive sales growth
- AI/ML Model Building: Utilize expertise in data science and machine learning to create scalable AI models specifically tailored for both online platforms and brick-and-mortar stores, aiming to improve operational efficiency and customer engagement.
- Ecommerce Optimization: Collaborate with cross-functional teams to implement data-driven solutions for personalized recommendations, demand forecasting, and dynamic pricing strategies to drive online sales and enhance customer satisfaction.
- Deep Learning & Image Recognition: Leverage advanced techniques in deep learning and image recognition to develop innovative solutions for product categorization, visual search, and inventory tracking, optimizing product discovery and inventory management.
- Python Development: Proficiency in Python programming is essential for designing, implementing, and maintaining robust and scalable machine learning pipelines and models, ensuring efficient deployment and integration into existing systems.
Qualifications and Experience:
- Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, or related fields.
- 3 to 4 years of hands-on experience in data science, machine learning, and deep learning, preferably within the ecommerce or retail sector.
- Proven track record of successful implementation of AI/ML models with tangible business impact.
- Strong proficiency in Python, along with familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Experience working with image data, including feature extraction, object detection, and classification.
- Excellent problem-solving skills, ability to work independently, and communicate complex technical concepts effectively to diverse stakeholders.
Roles and Responsibilities
- Managing available resources such as hardware, data, and personnel so that deadlines are met.
- Analyzing the ML and Deep Learning algorithms that could be used to solve a given problem and ranking them by their success probabilities
- Exploring data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
- Defining validation framework and establish a process to ensure acceptable data quality criteria are met
- Supervising the data acquisition and partnership roadmaps to create stronger product for our customers.
- Defining feature engineering process to ensure usage of meaningful features given the business constraints which may vary by market
- Device self-learning strategies through analysis of errors from the models
- Understand business issues and context, devise a framework for solving unstructured problems and articulate clear and actionable solutions underpinned by analytics.
- Manage multiple projects simultaneously while demonstrating business leadership to collaborate & coordinate with different functions to deliver the solutions in a timely, efficient and effective manner.
- Manage project resources optimally to deliver projects on time; drive innovation using residual resources to create strong solution pipeline; provide direction, coaching & training, feedbacks to project team members to enhance performance, support development and encourage value aligned behaviour of the project team members; Provide inputs for periodic performance appraisal of project team members.
Preferred Technical & Professional expertise
- Undergraduate Degree in Computer Science / Engineering / Mathematics / Statistics / economics or other quantitative fields
- At least 2+ years of experience of managing Data Science projects with specializations in Machine Learning
- In-depth knowledge of cloud analytics tools.
- Able to drive Python Code optimization; ability review codes and provide inputs to improve the quality of codes
- Ability to evaluate hardware selection for running ML models for optimal performance
- Up to date with Python libraries and versions for machine learning; Extensive hands-on experience with Regressors; Experience working with data pipelines.
- Deep knowledge of math, probability, statistics and algorithms; Working knowledge of Supervised Learning, Adversarial Learning and Unsupervised learning
- Deep analytical thinking with excellent problem-solving abilities
- Strong verbal and written communication skills with a proven ability to work with all levels of management; effective interpersonal and influencing skills.
- Ability to manage a project team through effectively allocation of tasks, anticipating risks and setting realistic timelines for managing the expectations of key stakeholders
- Strong organizational skills and an ability to balance and handle multiple concurrent tasks and/or issues simultaneously.
- Ensure that the project team understand and abide by compliance framework for policies, data, systems etc. as per group, region and local standards
Role and Responsibilities
- Execute data mining projects, training and deploying models over a typical duration of 2 -12 months.
- The ideal candidate should be able to innovate, analyze the customer requirement, develop a solution in the time box of the project plan, execute and deploy the solution.
- Integrate the data mining projects embedded data mining applications in the FogHorn platform (on Docker or Android).
Core Qualifications
Candidates must meet ALL of the following qualifications:
- Have analyzed, trained and deployed at least three data mining models in the past. If the candidate did not directly deploy their own models, they will have worked with others who have put their models into production. The models should have been validated as robust over at least an initial time period.
- Three years of industry work experience, developing data mining models which were deployed and used.
- Programming experience in Python is core using data mining related libraries like Scikit-Learn. Other relevant Python mining libraries include NumPy, SciPy and Pandas.
- Data mining algorithm experience in at least 3 algorithms across: prediction (statistical regression, neural nets, deep learning, decision trees, SVM, ensembles), clustering (k-means, DBSCAN or other) or Bayesian networks
Bonus Qualifications
Any of the following extra qualifications will make a candidate more competitive:
- Soft Skills
- Sets expectations, develops project plans and meets expectations.
- Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.
- Technical skills
- Commonly, candidates have a MS or Ph.D. in Computer Science, Math, Statistics or an engineering technical discipline. BS candidates with experience are considered.
- Have managed past models in production over their full life cycle until model replacement is needed. Have developed automated model refreshing on newer data. Have developed frameworks for model automation as a prototype for product.
- Training or experience in Deep Learning, such as TensorFlow, Keras, convolutional neural networks (CNN) or Long Short Term Memory (LSTM) neural network architectures. If you don’t have deep learning experience, we will train you on the job.
- Shrinking deep learning models, optimizing to speed up execution time of scoring or inference.
- OpenCV or other image processing tools or libraries
- Cloud computing: Google Cloud, Amazon AWS or Microsoft Azure. We have integration with Google Cloud and are working on other integrations.
- Decision trees like XGBoost or Random Forests is helpful.
- Complex Event Processing (CEP) or other streaming data as a data source for data mining analysis
- Time series algorithms from ARIMA to LSTM to Digital Signal Processing (DSP).
- Bayesian Networks (BN), a.k.a. Bayesian Belief Networks (BBN) or Graphical Belief Networks (GBN)
- Experience with PMML is of interest (see www.DMG.org).
- Vertical experience in Industrial Internet of Things (IoT) applications:
- Energy: Oil and Gas, Wind Turbines
- Manufacturing: Motors, chemical processes, tools, automotive
- Smart Cities: Elevators, cameras on population or cars, power grid
- Transportation: Cars, truck fleets, trains
About FogHorn Systems
FogHorn is a leading developer of “edge intelligence” software for industrial and commercial IoT application solutions. FogHorn’s Lightning software platform brings the power of advanced analytics and machine learning to the on-premise edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance and operational intelligence use cases. FogHorn’s technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as Smart Grid, Smart City, Smart Building and connected vehicle applications.
Press: https://www.foghorn.io/press-room/">https://www.foghorn.io/press-room/
Awards: https://www.foghorn.io/awards-and-recognition/">https://www.foghorn.io/awards-and-recognition/
- 2019 Edge Computing Company of the Year – Compass Intelligence
- 2019 Internet of Things 50: 10 Coolest Industrial IoT Companies – CRN
- 2018 IoT Planforms Leadership Award & Edge Computing Excellence – IoT Evolution World Magazine
- 2018 10 Hot IoT Startups to Watch – Network World. (Gartner estimated 20 billion connected things in use worldwide by 2020)
- 2018 Winner in Artificial Intelligence and Machine Learning – Globe Awards
- 2018 Ten Edge Computing Vendors to Watch – ZDNet & 451 Research
- 2018 The 10 Most Innovative AI Solution Providers – Insights Success
- 2018 The AI 100 – CB Insights
- 2017 Cool Vendor in IoT Edge Computing – Gartner
- 2017 20 Most Promising AI Service Providers – CIO Review
Our Series A round was for $15 million. Our Series B round was for $30 million October 2017. Investors include: Saudi Aramco Energy Ventures, Intel Capital, GE, Dell, Bosch, Honeywell and The Hive.
About the Data Science Solutions team
In 2018, our Data Science Solutions team grew from 4 to 9. We are growing again from 11. We work on revenue generating projects for clients, such as predictive maintenance, time to failure, manufacturing defects. About half of our projects have been related to vision recognition or deep learning. We are not only working on consulting projects but developing vertical solution applications that run on our Lightning platform, with embedded data mining.
Our data scientists like our team because:
- We care about “best practices”
- Have a direct impact on the company’s revenue
- Give or receive mentoring as part of the collaborative process
- Questions and challenging the status quo with data is safe
- Intellectual curiosity balanced with humility
- Present papers or projects in our “Thought Leadership” meeting series, to support continuous learning