{"id":3139151,"url":"https://alion.io/job/photon-aiml-data-scientist-onsite","title":"AI/ML Data Scientist | Onsite","company":{"id":3186451,"name":"Photon","domain":"photon.com","url":"https://alion.io/company/photon-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"D","score":51,"open_postings":701,"ghost_share":0.813,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dallas, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":40000,"max":null,"currency":"USD","period":"year","gross":null,"usd_annual":40000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"CNN","optional":false},{"name":"Computer Vision","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Seaborn","optional":false},{"name":"SHAP","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"Transformers","optional":false},{"name":"Agile","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-07-31T04:47:12Z","employer_posted_date":"2026-07-31","last_verified_at":"2026-10-28T04:18:54Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":88},"description":"Job Description: ML/AI Data Scientist - Predictive Modeling\nPosition Overview\nWe are seeking an experienced ML/AI Data Scientist to design, build, train, evaluate, and deploy predictive models that solve complex business and operational problems. The ideal candidate combines strong statistical foundations with hands-on experience in data analysis, feature engineering, model development, and performance evaluation.\nThis role is especially suited to someone who can build predictive solutions from the ground up and clearly explain the “what,” “why,” and “how” behind their analytical and modeling decisions to both technical and non-technical stakeholders.\nKey Responsibilities\nTranslate business problems into well-defined machine learning and predictive modeling objectives. \nCollect, clean, transform, and analyze structured and unstructured data from multiple sources. \nPerform exploratory data analysis to identify trends, relationships, anomalies, biases, and data-quality issues. \nDevelop predictive models from scratch, including data preparation, feature engineering, training, validation, testing, and optimization. \nBuild and apply regression models for forecasting, estimation, risk scoring, pricing, demand prediction, and related use cases. \nBuild and apply classification models for segmentation, fraud detection, churn prediction, recommendation, anomaly detection, and other decision-support applications. \nSelect appropriate algorithms based on the problem type, data characteristics, business requirements, interpretability needs, and operational constraints. \nCompare baseline, linear, tree-based, ensemble, and other appropriate modeling approaches. \nTune model hyperparameters and use appropriate cross-validation strategies to improve generalization. \nExperience building and deploying AI solutions using Natural Language Processing (NLP), Computer Vision, and sequence modeling techniques for text, image, video, and time-series data. \nStrong knowledge of deep learning architectures including RNNs, LSTMs, GRUs, CNNs, and Transformer-based models, with hands-on experience using TensorFlow or PyTorch. \nAbility to evaluate, optimize, and explain AI model performance, including model accuracy, robustness, bias detection, feature interpretation, and production monitoring. \nEvaluate model performance using relevant metrics such as RMSE, MAE, R², accuracy, precision, recall, F1 score, ROC-AUC, PR-AUC, log loss, calibration, and lift. \nAnalyze model errors and identify opportunities for improving data quality, features, sampling strategies, and model assumptions. \nAssess model robustness, explainability, fairness, stability, and sensitivity to changing data patterns. \nClearly communicate the rationale behind model selection, including why a particular model was chosen over alternatives. \nExplain technical results, assumptions, limitations, and trade-offs to product managers, business leaders, and other stakeholders. \nDocument analytical methods, data sources, assumptions, experiments, model decisions, and results. \nCollaborate with data engineers, software engineers, product teams, domain experts, and business stakeholders to operationalize models. \nSupport model deployment, monitoring, retraining, and continuous improvement in production environments. \nStay current with developments in machine learning, statistical modeling, AI techniques, and responsible AI practices. \nRequired Qualifications\nBachelor’s or master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline. \n3+ years of professional experience in data science, machine learning, predictive analytics, or a closely related field. \nStrong understanding of statistical analysis, probability, experimental design, and machine learning fundamentals. \nDemonstrated experience building predictive models from raw data through final evaluation. \nDeep practical expertise in regression and classification algorithms, including: \n- Linear and polynomial regression\n- Logistic regression\n- Regularization methods such as Ridge, Lasso, and Elastic Net\n- Decision trees\n- Random forests\n- Gradient boosting methods\n- Support vector machines\n- k-nearest neighbors\n- Naive Bayes\n- Ensemble modeling techniques\n- CNN\n- Computervision\n- RNN\n- NLP\nStrong knowledge of supervised learning workflows, including data splitting, cross-validation, feature selection, feature engineering, model tuning, and evaluation. \nProficiency in Python and common data science libraries such as pandas, NumPy, scikit-learn, SciPy, and matplotlib or Seaborn. \nStrong SQL skills and experience querying, joining, aggregating, and analyzing data from relational databases. \nExperience working with missing data, outliers, imbalanced classes, categorical variables, high-cardinality features, and data leakage risks. \nAbility to select and justify appropriate evaluation metrics based on business objectives and model use cases. \nExperience explaining model behavior using techniques such as feature importance, partial dependence, SHAP, coefficients, permutation importance, or related methods. \nExcellent written and verbal communication skills. \nAbility to present complex analytical concepts clearly to audiences with varying levels of technical expertise. \nPreferred Qualifications\nExperience deploying machine learning models through APIs, batch pipelines, or cloud-based platforms. \nFamiliarity with MLflow, Kubeflow, Airflow, Docker, Git, CI/CD, or similar tools. \nExperience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud. \nKnowledge of time-series forecasting, survival analysis, recommender systems, or anomaly detection. \nExperience with deep learning frameworks such as PyTorch or TensorFlow. \nFamiliarity with model monitoring, data drift, concept drift, model retraining, and performance degradation. \nExperience working with distributed data-processing tools such as Spark. \nKnowledge of responsible AI, model governance, fairness, privacy, and regulatory requirements. \nExperience working in an Agile or cross-functional product development environment. \nCore Competencies\nAnalytical Thinking\nAbility to break down ambiguous problems, identify relevant data, test assumptions, and develop rigorous analytical solutions.\nModel Selection and Justification\nAbility to explain why a specific model is appropriate based on accuracy, interpretability, scalability, latency, data volume, feature relationships, regulatory requirements, and business impact.\nStatistical and Technical Expertise\nStrong understanding of statistical concepts and practical machine learning methods, with the ability to distinguish correlation from causation and identify modeling limitations.\nData Understanding\nAbility to assess data quality, determine whether variables are meaningful, identify bias and leakage, and understand how data-generating processes affect model results.\nCommunication\nAbility to communicate model assumptions, results, trade-offs, uncertainty, and limitations in clear and accessible language.\nBusiness Orientation\nAbility to connect technical modeling outcomes to measurable business goals, operational decisions, customer outcomes, or financial impact.\nCollaboration\nAbility to work effectively with engineering, product, operations, and leadership teams throughout the model lifecycle.\nExpected Modeling Approach\nSuccessful candidates should be able to demonstrate a structured approach that includes:\nDefining the business problem and prediction target. \nEstablishing a simple and interpretable baseline. \nUnderstanding the data-generating process and identifying potential biases. \nPerforming exploratory data analysis. \nPreparing the data and engineering meaningful features. \nSelecting candidate models based on the problem and constraints. \nTraining and validating models using appropriate methodology. \nComparing models using business-relevant metrics. \nExplaining model behavior and identifying limitations. \nSelecting the final model based on accuracy, interpretability, reliability, and operational fit. \nDocumenting the decision-making process. \nMonitoring and improving the model after deployment. \nDeliverables and Success Measures\nHigh-quality exploratory analyses that produce actionable insights. \nReliable regression and classification models aligned with business objectives. \nClearly documented modeling decisions and assumptions. \nReproducible data preparation and model-training workflows. \nMeasurable improvements in forecasting accuracy, decision quality, efficiency, revenue, risk reduction, or customer outcomes. \nModels that are appropriately interpretable, robust, maintainable, and production-ready. \nClear communication of model performance, uncertainty, trade-offs, and limitations. \nEffective collaboration with stakeholders throughout the analytics and model development lifecycle. \nCompensation, Benefits and Duration\nMinimum Compensation: USD 40,000\nMaximum Compensation: USD 140,000\nCompensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.\nMedical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees.\nThis position is available for independent contractors\nNo applications will be considered if received more than 120 days after the date of this post","description_format":"text","description_chars":9457,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["401k plan","Retirement plans"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation"],"lifecycle":[{"event":"open","at":"2026-10-23T07:32:17Z"}],"visa":[],"liveness":{"score":11,"band":"cold","label":"Long shot","p_open":1,"p_active":0.378,"p_room":0.28,"age_days":88,"expected_fill_days":21,"reasons":["conf:19","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-27T05:46:45Z"},"pay":{"stated_usd_annual":40000,"is_top_pay":false},"html_url":"https://alion.io/job/photon-aiml-data-scientist-onsite","json_url":"https://alion.io/job/photon-aiml-data-scientist-onsite.json","meta":{"generated_at":"2026-10-28T04:41:21Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":6113,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-29T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3186451},"rest":"https://alion.io/mcp/rest/get_company?id=3186451"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fphoton-aiml-data-scientist-onsite"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fphoton-aiml-data-scientist-onsite"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fphoton-aiml-data-scientist-onsite"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/photon-aiml-data-scientist-onsite\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fphoton-aiml-data-scientist-onsite"}]}